164 citations · 526 across the 53 of their papers we have counts for
50 papers · 1 filter
What does two-sample Mendelian randomization estimate when the exposure-outcome relationship is nonlinear?
Eric J. Tchetgen Tchetgen
Two-sample Mendelian randomization with summary statistics has become a popular MR design in Epidemiology. Its ratio estimate has an average causal effect interpretation only under…
Regression-Based Proximal Reconciliation of Conflicting Trials with Unmeasured Effect Modifiers
Daniel A Xu, Eric J Tchetgen Tchetgen, Enrique F Schisterman +2
Randomized controlled trials with similar protocols may yield conflicting findings when the distribution of relevant effect modifiers differs across study populations. Yet no forma…
How should we select test-negative controls? A causal perspective in the era of multiplex respiratory testing
Christopher B. Boyer, Kendrick Qijun Li, Xu Shi +2
The test-negative design (TND) is widely used to estimate vaccine effectiveness (VE) for respiratory pathogens by comparing vaccination odds among test-positive cases versus test-n…
A General Exposure-Mapping-Agnostic Framework for Causal Inference under Interference
Yihui He, Eric J. Tchetgen Tchetgen
We develop a general framework for design-based causal inference under interference in cluster experiments conducted via two-stage randomization on a network of interconnected unit…
Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
Ting-Hsuan Chang, Caleb H. Miles, Ilya Shpitser +2
Causal graphs may inform covariate adjustment for estimating causal effects and improve estimation efficiency by exploiting the graphical structure. In many applications, however,…
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…
The Multiplicative Instrumental Variable Model
Jiewen Liu, Chan Park, Yonghoon Lee +4
The instrumental variable (IV) design is a common approach to address hidden confounding bias. For validity, an IV must impact the outcome only through its association with the tre…
Doubly Robust and Efficient Calibration of Prediction Sets for Right-Censored Time-to-Event Outcomes
Rebecca Farina, Eric J. Tchetgen Tchetgen, Arun Kumar Kuchibhotla
Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right-censoring with guaranteed coverage. Inspired by modern conformal inferenc…
Proximal Causal Inference for Conditional Separable Effects
Chan Park, Mats Stensrud, Eric Tchetgen Tchetgen
Scientists regularly pose questions about treatment effects on outcomes conditional on a post-treatment event. However, causal inference in such settings requires care, even in per…
Regression-Based Proximal Causal Inference
Jiewen Liu, Chan Park, Kendrick Li +1
Negative controls are increasingly used to evaluate the presence of potential unmeasured confounding in observational studies. Beyond the use of negative controls to detect the pre…
Proximal Causal Inference for Synthetic Control with Surrogates
Jizhou Liu, Eric J. Tchetgen Tchetgen, Carlos Varjão
The synthetic control method (SCM) has become a popular tool for estimating causal effects in policy evaluation, where a single treated unit is observed, and a heterogeneous set of…
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…
Doubly Robust Proximal Synthetic Controls
Hongxiang Qiu, Xu Shi, Wang Miao +2
To infer the treatment effect for a single treated unit using panel data, synthetic control methods construct a linear combination of control units' outcomes that mimics the treate…
Using Instruments for Selection to Adjust for Selection Bias in Mendelian Randomization
Apostolos Gkatzionis, Eric J. Tchetgen Tchetgen, Jon Heron +2
Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due…
A self-censoring model for multivariate nonignorable nonmonotone missing data
Yilin Li, Wang Miao, Ilya Shpitser +1
We introduce a self-censoring model for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome is affected by its own value and is associ…
Using negative controls to identify causal effects with invalid instrumental variables
Oliver Dukes, David B. Richardson, Zachary Shahn +2
Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. I…
Prediction Sets Adaptive to Unknown Covariate Shift
Hongxiang Qiu, Edgar Dobriban, Eric Tchetgen Tchetgen
Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructin…
Proximal mediation analysis
Oliver Dukes, Ilya Shpitser, Eric J. Tchetgen Tchetgen
A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In…
Proximal Causal Inference for Complex Longitudinal Studies
Andrew Ying, Wang Miao, Xu Shi +1
A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata,…
Semiparametric proximal causal inference
Yifan Cui, Hongming Pu, Xu Shi +2
Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchang…
A Selective Review of Negative Control Methods in Epidemiology
Xu Shi, Wang Miao, Eric Tchetgen Tchetgen
Purpose of Review: Negative controls are a powerful tool to detect and adjust for bias in epidemiological research. This paper introduces negative controls to a broader audience an…
Conditional separable effects
Mats J. Stensrud, James M. Robins, Aaron Sarvet +2
Researchers are often interested in treatment effects on outcomes that are only defined conditional on a post-treatment event status. For example, in a study of the effect of diffe…