5 papers
Causal Mediation Analysis for Panel Data with Double/Debiased Machine Learning
Peikai Wu, Zhiguo Xiao
We develop a double/debiased machine learning framework for causal mediation analysis in panel data with two-way fixed effects. Binary, multivalued, or continuous treatments and co…
Causal Mediation Analysis for Network Data with Graph Neural Network
Peikai Wu, Zhiguo Xiao
Causal mediation analysis is typically formulated under no interference, an assumption often violated in networked populations. We develop a nonparametric framework for a single la…
Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity
Peikai Wu, Kuan Sun, Zhiguo Xiao
We propose a double/debiased machine learning framework to estimate average derivative effects in nonparametric panel models with two-way fixed effects. It extends instrumental var…
Difference-in-Differences Under Network Interference
Kuan Sun, Zhiguo Xiao
This paper develops doubly robust estimators for direct (DATT) and spillover (SATT) average treatment effects on the treated in network-based difference-in-differences (DiD) design…
Causal Inference in Panel Data with a Continuous Treatment
Zhiguo Xiao, Peikai Wu
This paper proposes a framework that incorporates the two-way fixed effects model as a special case to conduct causal inference with a continuous treatment. Treatments are allowed…