Morris H. DeGroot Memorial Lecture - Eric Tchetgen Tchetgen

— 6:00pm

Location:
In Person - Poster Hall A35

Speaker:
ERIC J. TCHETGEN TCHETGEN , University Professor, Professor of Biostatistics in Biostatistics and Epidemiology, Professor of Statistics and Data Science, The Wharton School, University of Pennsylvania
https://statistics.wharton.upenn.edu/profile/ett/

On Identification in the Binary Instrumental Variable Model: Introducing the NATE and Beyond

We revisit the identification problem in the canonical binary instrumental variable model. Our work reveals new conditions for the classical Wald ratio estimand to be endowed with a nonparametric causal interpretation. Specifically, we describe a straightforward set of conditions under which the Wald Ratio point identifies the Nudge Average Treatment Effect (NATE), defined as the average causal effect for the subgroup of units whose treatment can be manipulated by the instrument, a sub-group referred to as Nudge-able. Crucially, the Nudge-able may include both compliers and defiers therefore obviating the need for the standard no-defier condition known to identify the Local Average Treatment Effect (LATE). 

Our key identification condition for the NATE is that any variability of the treatment effect induced by a hidden counfounder must be is uncorrelated with corresponding variability in the share of compliers among the Nudge-able. An important and easily interpretable sufficient condition for this assumption is that, although a priori unrestricted, the share of compliers within the subgroup of Nudge-able units is balanced across strata of the unmeasured confounders. Importantly, monotonicity is recovered as a degenerate case where the nudge-able are all compliers, thus ruling out the existence of defiers, in which case the NATE matches the LATE. 

Crucially, the Wald ratio retains a nonparametric causal interpretation as the NATE under the proposed identification condition, even when monotonicity does not hold,therefore providing a causal interpretation under weaker conditions than previously available. Various generalizations of the results will be given, including new straightforward conditions for identification of the average treatment effect for the treated by a generalized Wald ratio estimand, together with newquasi-IV identification results with an imperfect instrument which violates the exclusion restriction assumption. 

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Eric Tchetgen Tchetgen’s primary area of interest is in semi-parametric efficiency theory with application to causal inference, missing data problems, statistical genetics, and mixed model theory. In general, he works on the development of statistical and epidemiologic methods that make efficient useof the information in data collected by scientific investigators, while avoiding unnecessary assumptions about the underlying data generating mechanism. Dr. Tchetgen Tchetgen received his PhD from Harvard University. He is a co-winner of the 2022 Rousseeuw Prize for Statistics and was awarded the Myrto Lefkopoulou Distinguished Lectureship in 2020. 

The lecture is funded in part by the friends and colleagues of Morris H. DeGroot

Event Website:
https://www.cmu.edu/dietrich/statistics-datascience/events/index.html

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