Two-way fixed effects, the two-way mundlak regression, and difference-in-differences estimators

Abstract:
Abstract
                  I derive a result on the equivalence between the two-way fixed effects (TWFE) estimator and an estimator obtained from a pooled ordinary least squares regression that includes unit-specific time averages and time-period-specific cross-sectional averages, the two-way Mundlak (TWM) regression. The equivalence between TWFE and TWM implies that various estimators used for intervention analysis can be computed using pooled OLS that controls for time-constant treatment cohort indicators, time-period indicators, covariates, and interactions among them, allowing for considerable treatment effect heterogeneity. An extended version of TWFE (ETWFE) is equivalent to the POLS approach. I show that an imputation estimator, derived under no anticipation and parallel trends assumptions, is also equivalent to the POLS/ETWFE estimator. The equivalence among various estimators shows that average treatment effects on the treated are identified by flexible regression. The framework allows for event study estimators, which can be used to test for pre-trends, and flexible estimation that allows for cohort-specific trends.


Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs

Abstract: Abstract While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multiple pretreatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pretreatment periods: (1) assessing the parallel trends assumption, (2) improving estimation accuracy, and (3) allowing for a more flexible parallel trends assumption. We then propose a new estimator, double DID, which combines all the benefits through the generalized method of moments and contains the two-way fixed effects regression as a special case. We show that the double DID requires a weaker assumption about outcome trends and is more efficient than existing DID estimators. We also generalize the double DID to the staggered adoption design where different units can receive the treatment in different time periods. We illustrate the proposed method with two empirical applications, covering both the basic DID and staggered adoption designs. We offer an open-source R package that implements the proposed methodologies.


Data Sharing and Citation Impact in Visual Search Research: A Bibliometric Extension of Godwin et al. (2025)

Abstract: In the wake of psychology’s credibility revolution, open science practices such as data sharing have become more prevalent, yet researchers often cite a lack of professional incentives as a barrier to adoption. Building on a recent systematic audit of the visual search and eye-movement literature by Godwin et al. (2025), this report examines whether data sharing is associated with increased scholarly impact.We extended the dataset curated by Godwin et al. with bibliometric metadata from the OpenAlex registry and compared citation impact between articles that did and did not share data. Articles that shared data showed a small but significant advantage in field-weighted citation impact. Analyses of citation dynamics further indicated that this advantage was concentrated in the first four years post-publication and attenuated thereafter. In addition, citation impact did not vary as a function of data granularity: sharing fine-grained fixation-level data did not confer greater benefit than sharing coarser participant-level summaries.Although the analyses are observational and do not support causal inference, the findings suggest that data sharing is associated with a modest professional advantage, consistent with broader evidence linking open science practices to both individual recognition and cumulative scientific progress.


Bridging Structural Causal Inference and Machine Learning: The S-DIDML Estimator for Heterogeneous Treatment Effects

Asia Pacific Economic and Management Review

Abstract: In response to the increasing complexity of policy environments and the proliferation of high-dimensional data, this paper introduces the S-DIDML estimator, a framework grounded in structure and semiparametrically flexible for causal inference. By embedding Difference-in-Differences (DID) logic within a Double Machine Learning (DML) architecture, the S-DIDML approach combines the strengths of temporal identification, machine learning-based nuisance adjustment, and orthogonalized estimation. We begin by identifying critical limitations in existing methods, including the lack of structural interpretability in ML models, instability of classical DID under high-dimensional confounding, and the temporal rigidity of standard DML frameworks. Building on recent advances in staggered adoption designs and Neyman orthogonalization, S-DIDML offers a five-step estimation pipeline that enables robust estimation of heterogeneous treatment effects (HTEs) while maintaining interpretability and scalability. Demonstrative applications are discussed across labor economics, education, taxation, and environmental policy. The proposed framework contributes to the methodological frontier by offering a blueprint for policy-relevant, structurally interpretable, and statistically valid causal analysis in complex data settings.


Bias-Variance Tradeoff of Matching Prior to Difference-in-Differences When Parallel Trends is Violated

Abstract: Quasi-experimental causal inference methods have become central in empirical operations management (OM) for guiding managerial decisions. Among the relevant methods, empiricists utilize the Difference-in-Differences (DiD) estimator, which is built on the ``parallel trends” assumption. To increase the plausibility of parallel trends holding, researchers often match treated and control units before applying DiD, motivated by the intuition that matched groups are more likely to have the same post-treatment outcome absent treatment. Although this practice has been studied previously, the focus was solely on the bias. Yet bias is only one side to the story; the tradeoff in variance, and hence mean squared error (MSE), is left unexplored. In this work, we not only generalize earlier bias results under weaker assumptions but also fill this gap by analyzing properties of variance and MSE, a practically relevant metric for managerial decision making. We show, under a linear structural model with unobserved time-varying confounders, that variance results contrast with established bias insights: when considering variance, matching on observed covariates prior to DiD is not always recommended over the classic (unmatched) DiD due to a sample size tradeoff. Furthermore, matching additionally on pre-treatment outcome(s) is always beneficial as such tradeoff no longer exists once matching is performed. Given our novel contrasting results to the established literature, we characterize full bi


Parallel Trends in an Unparalleled Pandemic Difference-in-differences for infectious disease policy evaluation

Abstract: Researchers frequently employ difference-in-differences (DiD) to study the impact of public health interventions on infectious disease outcomes. DiD assumes that treatment and non-experimental comparison groups would have moved in parallel in expectation, absent the intervention (“parallel trends assumption”). However, the plausibility of parallel trends assumption in the context of infectious disease transmission is not well-understood. Our work bridges this gap by formalizing epidemiological assumptions required for common DiD specifications, positing an underlying Susceptible-Infectious-Recovered (SIR) data-generating process. We demonstrate that popular specifications can encode strict epidemiological assumptions. For example, DiD modeling incident case numbers or rates as outcomes will produce biased treatment effect estimates unless untreated potential outcomes for treatment and comparison groups come from a data-generating process with the same initial infection and equal transmission rates at each time step. Applying a log transformation or modeling log growth allows for different initial infection rates under an “infinite susceptible population” assumption, but invokes conditions on transmission parameters. We then propose alternative DiD specifications based on epidemiological parameters, the effective reproduction number and the effective contact rate, that are both more robust to differences between treatment and comparison groups and can be extended to complex


Causal Inference with Observational Data via Propensity Score Matching: A Simple, Hands‑On Guide

Abstract: Propensity score matching (PSM) was presented as a practical method for making fair comparisons in studies without random assignment. This paper explained PSM in accessible terms and provided a step-by-step workflow that could be followed by readers. The research was conducted to address the issue of differences between treated and untreated individuals. The key ideas behind PSM, the assumptions required for it to work, and approaches to verifying these assumptions with simple plots and summary statistics were described. Furthermore, the main steps were outlined: defining the target question, selecting baseline covariates, estimating the propensity score, performing matching or reweighting, checking balance, estimating the treatment effect, and conducting basic sensitivity analyses. To maintain a hands-on perspective, a small numerical example and a concise checklist were included to support replication in other studies. Besides, common pitfalls were discussed, such as weak overlap, overly strict calipers, and post-matching analyses that ignored the matched structure. This paper aimed to help students and applied researchers develop PSM analyses that were transparent, reproducible, and easy to communicate to non-specialists.


How Much Should We Trust Staggered Difference-In-Differences Estimates?

Abstract: Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the presence of treatment effect heterogeneity. Given the pronounced use of staggered treatment designs in applied corporate finance and accounting research, this finding potentially impacts a large swath of prior findings in these fields. We survey the nascent literature and document how and when such bias arises from treatment effect heterogeneity. We apply recently proposed methods to a set of prior published results, and find that correcting for the bias induced by the staggered nature of policy adoption frequently impacts the estimated effect from standard difference-in-difference studies. In many cases, the reported effects in prior research become indistinguishable from zero.


Difference-in-Differences with a Continuous Treatment

Brantly Callaway
Andrew Goodman-Bacon
Pedro H. C. Sant’Anna

Abstract: This paper analyzes difference-in-differences designs with a continuous treatment. We show that treatment-on-the-treated-type parameters are identified under a parallel trends assumption analogous to the binary treatment case. However, comparing these parameters across treatments is challenging because parallel trends does not rule out selection bias. We discuss alternative, typically stronger, assumptions that eliminate selection bias. We further show that popular two-way fixed effects estimands admit multiple interpretations, depending on the underlying causal building block, all having important limitations as meaningful summaries of treatment effects. Finally, we introduce alternative estimation procedures that avoid these drawbacks and demonstrate them in an empirical application.


Stacked Difference-in-Differences

Coady Wing
Seth Freedman
Alex Hollingsworth

Abstract: This paper introduces the concept of a "trimmed aggregate ATT," which is a weighted average of a set of group-time average treatment effect on the treated (ATT) parameters identified in a staggered adoption difference-in-differences (DID) design.The set of identified group-time ATTs that contribute to the aggregate is trimmed to achieve compositional balance across an event window, ensuring that comparisons of the aggregate parameter over event time reveal dynamic treatment effects and differential pre-trends rather than compositional changes.Taking the trimmed aggregate ATT as a target parameter, we investigate the performance of stacked DID estimators.We show that the most basic stacked estimator does not identify the target aggregate or any other average causal effect because it applies different implicit weights to treatment and control trends.The bias can be eliminated using corrective sample weights.We present a weighted stacked DID estimator, and show that it correctly identifies the target aggregate, providing justification for using the estimator in applied work.


Identification of Quantile Treatment Effects in Difference-in-Differences Settings with Staggered Adoption


Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption

Abstract: In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences estimator is is an unbiased estimator of a particular weighted average causal effect. We characterize the proeperties of this estimand, and show that the standard variance estimator is conservative.


Identification of quantile treatment effects in difference-in-differences settings with staggered adoption


When Can We Use Two-Way Fixed-Effects (TWFE): A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators

Abstract: The conventional Two-Way Fixed-Effects (TWFE) estimator has come under scrutiny lately. Recent literature has revealed potential shortcomings of TWFE when the treatment effects are heterogeneous. Scholars have developed new advanced dynamic Difference-in-Differences (DiD) estimators to tackle these potential shortcomings. However, confusion remains in applied research as to when the conventional TWFE is biased and what issues the novel estimators can and cannot address. In this study, we first provide an intuitive explanation of the problems of TWFE and elucidate the key features of the novel alternative DiD estimators. We then systematically demonstrate the conditions under which the conventional TWFE is inconsistent. We employ Monte Carlo simulations to assess the performance of dynamic DiD estimators under violations of key assumptions, which likely happens in applied cases. While the new dynamic DiD estimators offer notable advantages in capturing heterogeneous treatment effects, we show that the conventional TWFE performs generally well if the model specifies an event-time function. All estimators are equally sensitive to violations of the parallel trends assumption, anticipation effects or violations of time-varying exogeneity. Despite their advantages, the new dynamic DiD estimators tackle a very specific problem and they do not serve as a universal remedy for violations of the most critical assumptions. We finally derive, based on our simulations, recommendations for


Abstract: The conventional Two-Way Fixed-Effects (TWFE) estimator has been under strain lately. Recent literature has revealed potential shortcomings of TWFE when the treatment effects are heterogeneous. Scholars have developed new advanced dynamic Difference-in-Differences (DiD) estimators to tackle these potential shortcomings. However, confusion remains in applied research as to when the conventional TWFE is biased and what issues the novel estimators can and cannot address. In this study, we first provide an intuitive explanation of the problems of TWFE and elucidate the key features of the novel alternative DiD estimators. We then systematically demonstrate the conditions under which the conventional TWFE is inconsistent. We employ Monte Carlo simulations to assess the performance of dynamic DiD estimators under violations of key assumptions, which likely happens in applied cases. While the new dynamic DiD estimators offer notable advantages in capturing heterogeneous treatment effects, we show that the conventional TWFE performs generally well if the model specifies an event-time function. All estimators are equally sensitive to violations of the parallel trends assumption, anticipation effects or violations of time-varying exogeneity. Despite their advantages, the new dynamic DiD estimators tackle a very specific problem and they do not serve as a universal remedy for violations of the most critical assumptions. We finally derive, based on our simulations, recommendations for ho


Two-Way Fixed Effects and Difference-in-Differences Estimators with Heterogeneous Treatment Effects and Imperfect Parallel Trends

Clément de Chaisemartin
Xavier d'Haultfoeuille


Benchmarking parallel trends violations in regression imputation difference-in-differences

Abstract: Difference-in-differences studies increasingly use regression imputation methods as analternative to the conventional two-way fixed effects (TWFE) estimator, fitting a TWFEregression on the controls to impute treated counterfactuals. A common method for obtainingpre-trend placebo estimates uses the same model to impute outcomes for control units -the default in the popular fect R package. We decompose this “in-sample imputation”estimator into its component 2×2 differences-in-differences to show two severe biases: anattenuation bias driven by redundant differences-in-differences that are zero by constructionand a contamination bias resulting from the use of “early adopters” as controls. This leads tomisleading estimates of the magnitude and shape of the pre-treatment trends. “Leave-one-out” approaches address this, but only when done separately by treatment timing group. Ourresults suggest a trilemma: no single approach simultaneously avoids redundant comparisons,uses the same baseline periods as the treatment effects, and produces estimates for every pre-treatment period. We re-analyze a study on the political effects of the 2008 “shale shock” onRepublican vote share in U.S. coal counties (Gazmararian 2025). While the original analysisused in-sample imputation and concluded pre-trends were small, corrected approaches revealpre-trends comparable to the estimated treatment effects.


Comparative Evaluation of Difference in Differences Methods for Staggered Adoption Interventions

Ernesto Ulloa-Pérez
Elizabeth F. Bair
Amol S. Navathe
Kristin A. Linn

Abstract: Staggered adoption is a common approach for implementing healthcare interventions, where different units adopt the program at different times. Difference-in-differences (DiD) methods are frequently used to evaluate the effects of such interventions. Nonetheless, recent research has shown that classical DiD approaches designed for a single treatment start date can produce biased estimates in staggered adoption settings, particularly due to treatment effect heterogeneity across adoption and calendar time. Several alternative methods have been developed to address these limitations. However, these methods have not been fully systematically compared, and their practical utility remains unclear. Motivated by a payment program implemented by a healthcare provider in Hawaii, we provide a comprehensive review of the staggered adoption setting and a selection of DiD methods suitable for this context. We begin with a theoretical overview of these methods, followed by a simulation study designed to resemble the characteristics of our application, where the intervention is implemented at the cluster level. Our results show that the current methods tend to under-perform when the number of clust


Benefits and costs of matching prior to a difference in differences analysis when parallel trends does not hold


A Generalized Difference-in-Differences Estimator for Randomized Stepped-Wedge and Observational Staggered Adoption Settings

Abstract: Staggered treatment adoption arises in the evaluation of policy impact and implementation in many settings, including both randomized stepped-wedge trials and non-randomized quasi-experiments with panel data. In both settings, getting an interpretable, unbiased effect estimate requires careful consideration of the target estimand and possible treatment effect heterogeneities. This paper proposes a novel non-parametric approach to this estimation for either setting. By constructing an estimator using weighted averages of two-by-two difference-in-differences comparisons as building blocks, the investigator can target the desired estimand for any assumed treatment effect heterogeneities. This provides desirable bias and interpretation properties while using the comparisons efficiently to mitigate the loss of precision, without requiring correct variance specification. The methods are demonstrated for both a randomized stepped-wedge trial on the impact of novel tuberculosis diagnostic tools and an observational staggered adoption study on the effects of COVID-19 vaccine financial incentive lotteries in U.S. states; these are compared to analyses using previous methods. A full algorithm


Evaluating policy impact under sparse and staggered adoption. A synthetic difference-in-differences application to EU rural development measures.

Abstract: This paper investigates how farmers respond to targeted policy measures under the EU Common Agricultural Policy (CAP), focusing on whether voluntary adoption, often driven by private motivations, also leads to outcomes of societal interest. To address the methodological challenges posed by staggered and sparse treatment adoption, the study employs a Synthetic Difference-in-Differences (SDID) approach. A theoretical framework is developed to distinguish between private and societal outcomes of policy adoption. The empirical analysis uses a balanced panel of Italian farms from 2014 to 2022 and focuses on selected second-pillar CAP measures. Results reveal that while some measures significantly affect private outcomes (e.g., farm income or productivity), their impact on societal outcomes (e.g., environmental indicators) is weaker and more volatile. The paper discusses key challenges in identifying and estimating effects with few, heterogeneous treated units and staggered policy uptake. While SDID is well-suited to such contexts, its real-world application may face practical limitations.


Investigating Impacts of Health Policies Using Staggered Difference-in-Differences: The Effects of Adoption of an Online Consultation System on Prescribing Patterns of Antibiotics

Stephen O'Neill Department of Medical Statistics

Abstract: We use a recently proposed staggered difference-in-differences approach to investigate effects of adoption of an online consultation system in English general practice on antibiotic prescribing patterns. The target estimand is the average effect for each group of practices (defined by year of adoption) in each year, which we aggregate across all adopting practices, by group, and by time since adoption. We find strong evidence of a positive effect of adoption on antibiotic prescribing rates, though the magnitude of effect is relatively small. As time since adoption increases, the effect size increases, while effects vary across groups.


Bayesian Estimation of Cohort-Time-Stratum Specific Effects in Staggered Difference-in-Differences

Abstract: Difference-in-Differences designs with staggered treatment adoption are widely used to study heterogeneous treatment effects across cohorts and time periods. We develop a probabilistic framework for estimating potentially high-dimensional ATT arrays that vary across cohorts, periods, and strata defined by baseline covariates. The framework jointly estimates subgroup-specific treatment effects through a unified likelihood-based model, stabilizing inference in sparse cohort-by-time-by-stratum settings. We establish a Bernstein-von Mises theorem for the ATT array, implying asymptotically valid frequentist coverage of posterior credible intervals. Simulations and an application to minimum wage increases and teen employment demonstrate meaningful finite-sample improvements and important subgroup heterogeneity.


Understanding Difference-in-differences methods to evaluate policy effects with staggered adoption: an application to Medicaid and HIV

Julia C. Thome
P. Rebeiro
Andrew J. Spieker
Bryan E. Shepherd

Abstract: While a randomized control trial is considered the gold standard for estimating causal treatment effects, there are many research settings in which randomization is infeasible or unethical. In such cases, researchers rely on analytical methods for observational data to explore causal relationships. Difference-in-differences (DID) is one such method that, most commonly, estimates a difference in some mean outcome in a group before and after the implementation of an intervention or policy and compares this with a control group followed over the same time (i.e., a group that did not implement the intervention or policy). Although DID modeling approaches have been gaining popularity in public health research, the majority of these approaches and their extensions are developed and shared within the economics literature. While extensions of DID modeling approaches may be straightforward to apply to observational data in any field, the complexities and assumptions involved in newer approaches are often misunderstood. In this paper, we focus on recent extensions of the DID method and their relationships to linear models in the setting of staggered treatment adoption over multiple years. We detail the identification and estimation of the average treatment effect among the treated using potential outcomes notation, highlighting the assumptions necessary to produce valid estimates. These concepts are described within the context of Medicaid expansion and retention in care among people l


Synthetic difference-in-differences estimation with staggered treatment timing


What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature

Jonathan Roth
Pedro H. C. Sant’Anna
Alyssa Bilinski
John Poe

Abstract: This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of ``canonical'' assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on $(i)$ multiple periods and variation in treatment timing, $(ii)$ potential violations of parallel trends, or $(iii)$ alternative frameworks for inference. Our discussion highlights the different ways that the DiD literature has advanced beyond the canonical model, and helps to clarify when each of the papers will be relevant for empirical work. We conclude by discussing some promising areas for future research.


Synthetic Difference-In-Differences Estimation With Staggered Treatment Timing


DiDforBigData: A Big Data Implementation of Difference-in-Differences Estimation with Staggered Treatment


Tablets Behind Bars: Evidence from a Staggered Adoption Difference-in-Differences Study of Prison Misconduct

Bryce Peterson
KiDeuk Kim
Rochisha Shukla
Sarah Aukamp


Modern Staggered Difference-in-Differences: From the Pitfalls of Two-Way Fixed Effects (TWFE) to Robust Estimation


Parallel Trends Forest: Data-Driven Control Sample Selection in Difference-in-Differences


Review 2: "Parallel Trends in an Unparalleled Pandemic: Difference-in-Differences for Infectious Disease Policy Evaluation"


Review 1: "Parallel Trends in an Unparalleled Pandemic: Difference-in-Differences for Infectious Disease Policy Evaluation"


Reviews of "Parallel Trends in an Unparalleled Pandemic: Difference-in-Differences for Infectious Disease Policy Evaluation"


Interpretation of Nonlinear Difference-in-Differences: The Role of the Parallel Trends Assumption


Cannabis legalization and cannabis and opioid use in a large, multistate sample of people who inject drugs: A staggered adoption difference-in-differences analysis.

Danielle F. Haley
Stephanie Beane
L. Beletsky
Courtney Yarbrough
Sabriya L Linton
Umedjon Ibragimov
H. Cooper

Abstract:
BACKGROUND
The role of cannabis reform in shaping North America's overdose and addiction crisis remains hotly contested. People who inject drugs (PWID) sometimes substitute cannabis for opioids. Yet, no research has examined the effects of cannabis legalization on opioid use among PWID-- despite major potential for PWID to benefit from policy interventions reducing opioid-related harms. We examined whether legalizing cannabis for medical use (MCL) vs. both MCL and adult/recreational use (MCL+RCL) was associated with changes in substance use among PWID, overall and by sex and race/ethnicity.

METHODS
This serial cross-sectional observational study used staggered adoption difference-in-differences models to analyze data (2012, 2015, 2018, 2022) from 28,069 PWID from 13 states. We examined associations between time-varying implementation of MCLs and MCL+RCLs and self-reported past 12-month daily use of cannabis and non-medical opioid use.

RESULTS
Compared to MCL, MCL+RCL was associated with a 9-11 % decrease in the probability of daily opioid misuse (95 % CIs: [any opioids: -14.0, -4.0]; [injected opioids, -19.0, -2.0]). The probability of daily cannabis use increased from 15 % to 20 % for non-Latinx White PWID in states transitioning from no legalization to MCL (95 % CIs: 14.0, 17.0; 17.0, 23.0).

CONCLUSIONS
Cannabis legalization may shape daily opioid consumption among PWID, potentially reducing drug-related harms. Differences in cannabis use following legalization may re


Covariate Balancing Propensity Score

Journal of the Royal Statistical Society Series B (Statistical Methodology)

Abstract: Summary The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias of estimated treatment effects. We introduce covariate balancing propensity score (CBPS) methodology, which models treatment assignment while optimizing the covariate balance. The CBPS exploits the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment. The estimation of the CBPS is done within the generalized method-of-moments or empirical likelihood framework. We find that the CBPS dramatically improves the poor empirical performance of propensity score matching and weighting methods reported in the literature. We also show that the CBPS can be extended to other important settings, including the estimation of the generalized propensity score for non-binary treatments and the generalization of experimental estimates to a target population. Open source software is available for implementing the methods proposed.


To Bootstrap or Not in Economic Modeling: The Impact Of Propensity Score Matching within Causal Inference Framework


Citation-based clustering of publications using CitNetExplorer and VOSviewer

Abstract: Clustering scientific publications in an important problem in bibliometric research. We demonstrate how two software tools, CitNetExplorer and VOSviewer, can be used to cluster publications and to analyze the resulting clustering solutions. CitNetExplorer is used to cluster a large set of publications in the field of astronomy and astrophysics. The publications are clustered based on direct citation relations. CitNetExplorer and VOSviewer are used together to analyze the resulting clustering solutions. Both tools use visualizations to support the analysis of the clustering solutions, with CitNetExplorer focusing on the analysis at the level of individual publications and VOSviewer focusing on the analysis at an aggregate level. The demonstration provided in this paper shows how a clustering of publications can be created and analyzed using freely available software tools. Using the approach presented in this paper, bibliometricians are able to carry out sophisticated cluster analyses without the need to have a deep knowledge of clustering techniques and without requiring advanced computer skills.


Towards a generalized framework for missing value imputation of fragmented Earth observation data

Verena Bessenbacher
Lukas Gudmundsson
Sonia I. Seneviratne

Abstract: &lt;p&gt;&lt;span&gt;The past decades have seen massive advances in generating Earth System observations. A plethora of instruments is, at any point in time, taking remote measurements of the Earth&amp;#8217;s surface aboard satellites. This birds-eye view of the land surface has become invaluable to the climate science and hydrology communities. However, the same variable is often observed by several platforms&lt;/span&gt;&lt;!-- Add that fragmentation also stems from observing different vars with not comparable measurement techniques, not necessarly ensuring physical consistency VB --&gt;&lt;span&gt; with contrasting results and satellite observations have non-trivial patterns of missing values. Consequently, mostly only one remote sensing product is used simultaneously. This and the inherent missingness of the datasets has led to a fragmentation of the observational record that limits the widespread use of remotely sensed land observations. We aim towards a generalized framework for gap-filling global, high-resolution remote sensing measurements relevant for the terrestrial water cycle, focusing on ESA microwave soil moisture, land surface temperature and GPM precipitation. To this end, we explore statistical imputation methods and benchmark them using a &amp;#8220;perfect dataset approach&amp;#8221;, in which we apply the missingness pattern of the remote sensing datasets onto their matching variables in the ERA