7 citations · 12 across the 9 of their papers we have counts for
10 papers
Randomization Inference For the Always-Reporter Average Treatment Effect
Haoge Chang, Zeyang Yu
This article studies randomization inference for treatment effects in randomized controlled trials with attrition, where outcomes are observed for only a subset of units. We assume…
On the Foundations of the Design-Based Approach
P. M. Aronow, Austin Jang, Molly Offer-Westort
The design-based paradigm may be adopted in causal inference and survey sampling when we assume Rubin's stable unit treatment value assumption (SUTVA) or impose similar frameworks.…
A Meta-learner for Heterogeneous Effects in Difference-in-Differences
Hui Lan, Haoge Chang, Eleanor Dillon +1
We address the problem of estimating heterogeneous treatment effects in panel data, adopting the popular Difference-in-Differences (DiD) framework under the conditional parallel tr…
Randomization-based confidence sets for the local average treatment effect
P. M. Aronow, Haoge Chang, Patrick Lopatto
We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens…
AI Revolution on Chat Bot: Evidence from a Randomized Controlled Experiment
Sida Peng, Wojciech Swiatek, Allen Gao +2
In recent years, generative AI has undergone major advancements, demonstrating significant promise in augmenting human productivity. Notably, large language models (LLM), with Chat…
Design-based Estimation Theory for Complex Experiments
Haoge Chang
This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a…
Fast computation of exact confidence intervals for randomized experiments with binary outcomes
P. M. Aronow, Haoge Chang, Patrick Lopatto
Given a randomized experiment with binary outcomes, exact confidence intervals for the average causal effect of the treatment can be computed through a series of permutation tests.…
Approximating Choice Data by Discrete Choice Models
Haoge Chang, Yusuke Narita, Kota Saito
We obtain a necessary and sufficient condition under which random-coefficient discrete choice models, such as mixed-logit models, are rich enough to approximate any nonparametric r…
Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials
Haoge Chang, Joel Middleton, P. M. Aronow
In an influential critique of empirical practice, Freedman (2008) showed that the linear regression estimator was biased for the analysis of randomized controlled trials under the…
Design-Based Inference for Spatial Experiments under Unknown Interference
Ye Wang, Cyrus Samii, Haoge Chang +1
We consider design-based causal inference for spatial experiments in which treatments may have effects that bleed out and feed back in complex ways. Such spatial spillover effects…