35 citations · 38 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Generalized Bayes inference on a linear personalized minimum clinically important difference
Pei-Shien Wu, Ryan Martin
Inference on the minimum clinically important difference, or MCID, is an important practical problem in medicine. The basic idea is that a treatment being statistically significant…
Calibrating generalized predictive distributions
Pei-Shien Wu, Ryan Martin
In prediction problems, it is common to model the data-generating process and then use a model-based procedure, such as a Bayesian predictive distribution, to quantify uncertainty…
A comparison of learning rate selection methods in generalized Bayesian inference
Pei-Shien Wu, Ryan Martin
Generalized Bayes posterior distributions are formed by putting a fractional power on the likelihood before combining with the prior via Bayes's formula. This fractional power, whi…