“On Identification in the Binary Instrumental Variable Model: Introducing the NATE,” Eric J. Tchetgen Tchetgen, UPenn
QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: The instrumental variable method is a prominent approach to recover under certain conditions, valid inference about a treatment causal effect even when unmeasured confounding might be present. In a highly celebrated paper, Imbens and Angrist (1994) established that a valid instrument nonparametrically identifies the average causal effect among compliers, also known as the local average treatment effect (LATE) under a certain monotonicity assumption which rules out the existence of so-called defiers. An often-cited attractive property of monotonicity is that it facilitates a causal interpretation of the instrumental variable estimand without restricting the degree of heterogeneity of the treatment causal effect. In this paper, we introduce an alternative equally straightforward and interpretable condition for identification, which accommodates both the presence of defiers and heterogenous treatment effects. We show that under our new conditions, the instrumental variable estimand recovers the average causal effect for the subgroup of units for whom the treatment is manipulable by the instrument, a subgroup which may consist of both defiers and compliers, therefore recovering an effect estimand we aptly call the Nudge Average Treatment Effect (NATE). Notably, our conditions are substantially weaker than monotonicity which is recovered as a special corner case, where the NATE matches the LATE; crucially, the IV estimand remains interpretable as the NATE under our conditions even if monotonicity fails.
Eric J. Tchetgen Tchetgen is The University Professor, Professor of Biostatistics at the Perelman School of Medicine, and Professor of Statistics and Data Science at The Wharton School, University of Pennsylvania. He co-directs the Penn Center for Causal Inference, a leading initiative dedicated to advancing and disseminating causal inference methodologies in the health and social sciences. Professor Tchetgen Tchetgen is a leading expert in Causal Inference, Missing Data, and Semiparametric Theory, with impactful applications in HIV research, Genetic Epidemiology, Environmental Health, and Alzheimer’s Disease and related aging disorders. He is an Amazon scholar working with Amazon scientists, applying causal inference techniques to address complex challenges in the tech industry.
In recognition of his groundbreaking research, Professor Tchetgen Tchetgen was a co-recipient of the inaugural 2022 Rousseeuw Prize awarded to Causal Inference for pioneering research on causal inference with real-world applications in medicine and public health. In 2024, he was awarded the Marshall Joffe Epidemiologic Methods Research Award by the Society of Epidemiologic Research; and in 2025, he was awarded the inaugural David Cox Medal for Statistics, which celebrates exceptional mid-career researchers for their outstanding contributions to the field. Professor Tchetgen Tchetgen received this honor in recognition of his groundbreaking contributions to statistical theory and methodology, particularly in causal inference. His work has significantly advanced the discipline, most notably through the development of Proximal Causal Inference and instrumental variable methodology – two essential frameworks for addressing confounding in causal analysis.
This workshop is open to the Yale community. To receive announcements and invitations to attend, please subscribe at https://csap.yale.edu/quantitative-research-methods-workshop.
The Quantitative Research Methods Workshop series is sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.