21 citations · 38 across the 13 of their papers we have counts for
13 papers
Sliced Distributional Balancing
Haoran Zhang, Guanhua Chen, Chan Park
A popular class of causal inference methods addresses confounding through weighting, which reweights treated and control groups to balance their covariate distributions without usi…
The Multiplicative Quasi-Instrumental Variable Model
Jiewen Liu, Chan Park, David Richardson +1
We introduce the Multiplicative Quasi-Instrumental Variable (MQIV) model, a framework for causal inference with unmeasured confounding that leverages an instrument that may be impe…
Single Proxy Synthetic Control
Chan Park, Eric Tchetgen Tchetgen
Synthetic control methods are widely used to estimate the treatment effect on a single treated unit in time-series settings. A common approach to estimate synthetic control weights…
Single Proxy Control
Chan Park, David Richardson, Eric Tchetgen Tchetgen
Negative control variables are sometimes used in non-experimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome th…
Universal Difference-in-Differences for Causal Inference in Epidemiology
Eric Tchetgen Tchetgen, Chan Park, David Richardson
Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. Th…
A Nonparametric Framework for Universal Difference-in-Differences
Chan Park, Eric Tchetgen Tchetgen
Difference-in-differences (DiD) is a popular approach to evaluate treatment effects in settings where both pre- and post-treatment measurements of the outcome are available. Despit…
Model-robust and efficient covariate adjustment for cluster-randomized experiments
Bingkai Wang, Chan Park, Dylan S. Small +1
Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjust…
Propensity Score Modeling: Key Challenges When Moving Beyond the No-Interference Assumption
Hyunseung Kang, Chan Park, Ralph Trane
The paper presents some models for the propensity score. Considerable attention is given to a recently popular, but relatively under-explored setting in causal inference where the…
Minimum Resource Threshold Policy Under Partial Interference
Chan Park, Guanhua Chen, Menggang Yu +1
When developing policies for prevention of infectious diseases, policymakers often set specific, outcome-oriented targets to achieve. For example, when developing a vaccine allocat…
A More Efficient, Doubly Robust, Nonparametric Estimator of Treatment Effects in Multilevel Studies
Chan Park, Hyunseung Kang
When studying treatment effects in multilevel studies, investigators commonly use (semi-)parametric estimators, which make strong parametric assumptions about the outcome, the trea…
Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects
Chan Park, Hyunseung Kang
Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on st…
Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interference
Chan Park, Hyunseung Kang
Recently, many estimators for network treatment effects have been proposed. But, their optimality properties in terms of semiparametric efficiency have yet to be resolved. We prese…
A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference
Chan Park, Hyunseung Kang
Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often ha…