10 citations · 28 across the 16 of their papers we have counts for
16 papers
Unbiased Treatment Effect Estimation under Network Interference via Neighborhood-Excluded Cross-Fitting
Haoyang Yu, Anqi Zhao, Hanzhong Liu
Without interference, cross-fitting enables flexible covariate adjustment while preserving finite-sample unbiasedness under independent unit-level randomization. Under network inte…
Design-based theory for causal inference from adaptive experiments
Xinran Li, Anqi Zhao
Adaptive designs dynamically update treatment probabilities using information accumulated during the experiment. Existing theory for causal inference from adaptive experiments prim…
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…
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…
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…
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…
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…
A randomization-based theory for preliminary testing of covariate balance in controlled trials
Anqi Zhao, Peng Ding
Randomized trials balance all covariates on average and provide the gold standard for estimating treatment effects. Chance imbalances nevertheless exist more or less in realized tr…
Rerandomization and covariate adjustment in split-plot designs
Wenqi Shi, Anqi Zhao, Hanzhong Liu
The split-plot design arises from agricultural sciences with experimental units, also known as subplots, nested within groups known as whole plots. It assigns the whole-plot interv…
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…
No star is good news: A unified look at rerandomization based on -values from covariate balance tests
Anqi Zhao, Peng Ding
Modern social and biomedical scientific publications require the reporting of covariate balance tables with not only covariate means by treatment group but also the associated -…
To adjust or not to adjust? Estimating the average treatment effect in randomized experiments with missing covariates
Anqi Zhao, Peng Ding
Complete randomization allows for consistent estimation of the average treatment effect based on the difference in means of the outcomes without strong modeling assumptions on the…
Reconciling design-based and model-based causal inferences for split-plot experiments
Anqi Zhao, Peng Ding
The split-plot design assigns different interventions at the whole-plot and sub-plot levels, respectively, and induces a group structure on the final treatment assignments. A commo…
Regression-based causal inference with factorial experiments: estimands, model specifications, and design-based properties
Anqi Zhao, Peng Ding
Factorial designs are widely used due to their ability to accommodate multiple factors simultaneously. The factor-based regression with main effects and some interactions is the do…
Covariate-adjusted Fisher randomization tests for the average treatment effect
Anqi Zhao, Peng Ding
Fisher's randomization test (FRT) delivers exact -values under the strong null hypothesis of no treatment effect on any units whatsoever and allows for flexible covariate adjust…
Randomization-Based Causal Inference from Unbalanced 2^2 Split-Plot Designs
Anqi Zhao, Peng Ding, Tirthankar Dasgupta
Given two 2-level factors of interest, a 2^2 split-plot design} (a) takes each of the possible factorial combinations as a treatment, (b) identifies one factor as `whole-pl…