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20212026
most citedFactorial Difference-in-Differences

4 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2026

Two-stage Least Squares with Clustered Data under the Local Average Treatment Effect Framework

Anqi Zhao, Peng Ding, Fan Li

To estimate the causal effect of an endogenous treatment using clustered data, the canonical two-stage least squares (2sls) estimates a linear regression of the outcome on treatmen…

stat.ME2025

Alternative statistical inference for the first normalized incomplete moment

Jiannan Lu, Peng Ding, Anqi Zhao

This paper re-examines the first normalized incomplete moment, a well-established measure of inequality with wide applications in economic and social sciences. Despite the populari…

stat.ME2025

Two-stage least squares with treatment-covariate interactions for treatment effect heterogeneity

Anqi Zhao, Peng Ding, Fan Li

Treatment effect heterogeneity with respect to covariates is common in instrumental variable (IV) analyses. An intuitive approach, which we call the interacted two-stage least squa…

stat.ME2024★ 4 cited

Factorial Difference-in-Differences

Yiqing Xu, Anqi Zhao, Peng Ding

We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. I…

stat.ME2023

Covariate adjustment in randomized experiments with missing outcomes and covariates

Anqi Zhao, Peng Ding, Fan Li

Covariate adjustment can improve precision in analyzing randomized experiments. With fully observed data, regression adjustment and propensity score weighting are asymptotically eq…

stat.ME2021★ 2 cited

Covariate adjustment in multi-armed, possibly factorial experiments

Anqi Zhao, Peng Ding

Randomized experiments are the gold standard for causal inference, and justify simple comparisons across treatment groups. Regression adjustment provides a convenient way to incorp…