12 citations · 37 across the 9 of their papers we have counts for
9 papers
Refining the Notion of No Anticipation in Difference-in-Differences Studies
Marco Piccininni, Eric J. Tchetgen Tchetgen, Mats J. Stensrud
We clarify the interpretation of identification strategies using difference-in-differences (DiD), which are widely applied in empirical research. In particular, DiD researchers oft…
Fortified Proximal Causal Inference with Many Invalid Proxies
Myeonghun Yu, Xu Shi, Eric J. Tchetgen Tchetgen
Causal inference from observational data often relies on the assumption of no unmeasured confounding, an assumption frequently violated in practice due to unobserved or poorly meas…
Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei
Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen
This note has been updated (April, 2020) to respond to "Towards Clarifying the Theory of the Deconfounder" by Yixin Wang, David M. Blei (arXiv:2003.04948). This original note, post…
Marginal Structural Models for Time-varying Endogenous Treatments: A Time-Varying Instrumental Variable Approach
Eric J Tchetgen Tchetgen, Haben Michael, Yifan Cui
Robins (1998) introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal…
Doubly Robust Regression Analysis for Data Fusion
Katherine Evans, BaoLuo Sun, James Robins +1
This paper investigates the problem of making inference about a parametric model for the regression of an outcome variable on covariates when data are fused from two se…
A general approach to detect gene (G)-environment (E) additive interaction leveraging G-E independence in case-control studies
Eric J. Tchetgen Tchetgen, Xu Shi, Tamar Sofer +1
It is increasingly of interest in statistical genetics to test for the presence of a mechanistic interaction between genetic (G) and environmental (E) risk factors by testing for t…
Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding
Xu Shi, Wang Miao, Jennifer C. Nelson +1
Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing…
A causal framework for classical statistical estimands in failure time settings with competing events
Jessica G. Young, Mats J. Stensrud, Eric J. Tchetgen Tchetgen +1
In failure-time settings, a competing risk event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing…
On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding
Caleb H. Miles, Ilya Shpitser, Phyllis Kanki +2
Path-specific effects are a broad class of mediated effects from an exposure to an outcome via one or more causal pathways with respect to some subset of intermediate variables. Th…