Causal Inference

Matching Estimation for Causal Effect on Compositional Outcomes

Kenichi Ariga   (University of Toronto)

Abstract: Compositional outcomes are not unusual in political science research....

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Gaussian Process Models for Causal Inference With Time-Series Cross-Sectional Data

Nuannuan Xiang and Kevin Quinn (University of Michigan)

Abstract: In this paper, we develop a class of Gaussian Process models to estimate treatment effects with time-series cross-sectional data, in which a subset of units receives treatment in a subset of time periods. We impute potential (untreated) outcomes of...

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Causal Inference Under Temporal and Spatial Interference

Ye Wang (New York University)

Abstract: Many social events and policies generate spillover effects in both time and space. Their occurrence influences not only the outcomes of interest in the future, but also these outcomes in nearby areas. In this paper, I propose a semi-parametric approach to estimate the direct and indirect/...

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Causal Inference in Difference-in-Differences Designs under Uncertainty in Counterfactual Trends

Thomas Leavitt (Columbia University)

Abstract: Difference-in-Differences (DID) is a popular method for design-based causal inference. Design-based methods typically quantify uncertainty in inferences from a sample to a population via a sampling mechanism and from observed to counterfactual outcomes via an assignment mechanism. The...

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A General Method for Detecting & Characterizing Interference in Field Experiments

Connor Jerzak (Harvard University)

Abstract: With the rise of online social networks, there has been growing interest in modeling how experimental units influence each another---a phenomenon known as "interference'' in the causal inference literature. Current models for interference generally requires knowledge of the way in which units are connected. Yet, in most field experiments, such data is unavailable. In this paper, we propose a...

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Leveraging Observational Outcomes to Improve the Generalization of Experimental Results

Melody Huang (University of California, Los Angeles), Erin Hartman (University of California, Los Angeles), Naoki Egami (Columbia University) and Luke Miratrix (Harvard University)

Abstract: Randomized control...

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Priming Bias Versus Post-Treatment Bias in Experimental Designs

Jacob Brown (Harvard University), Matthew Blackwell (Harvard University), Sophie Hill (Harvard University), Kosuke Imai (Harvard University) and Teppei Yamamoto (Massachusetts Institute of Technology)

Abstract:  It is now widely recognized that conditioning on variables affected by a treatment can induce post-treatment bias when estimating causal...

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