Virtual Room 4: Instrumental Variables


Thursday, July 16, 2020, 2:30pm to 4:00pm


Chair: Jonathan N. Katz (California Institute of Technology)


Co-Host: Justin Savoie (University of Toronto)

An omitted variable bias framework for sensitivity analysis of instrumental variables

Author(s): Carlos Cinelli and Chad Hazlett

Discussant: Jacob Montgomery (WUSTL)


We develop an omitted variable bias framework for sensitivity analysis of instrumental variable (IV) estimates that is immune to "weak instruments," naturally handles multiple "side-effects" and "confounders," exploits expert knowledge to bound sensitivity parameters, and can be easily implemented with standard software. In particular, we introduce sensitivity statistics for routine reporting, such as robustness values for IV estimates, describing the minimum strength that omitted variables need to have to change the conclusions of a study. We show how these depend upon the sensitivity of two familiar auxiliary estimates–the effect of the instrument on the treatment (the "first-stage") and the effect of the instrument on the outcome (the "reduced form")–and how an extensive set of sensitivity questions can be answered from those alone. Next, we provide tools that fully characterize the sensitivity of point-estimates and confidence intervals to violations of the standard IV assumptions. Finally, we offer formal bounds on the worst damage caused by these violations by means of comparisons with the explanatory power of observed variables. We illustrate our tools with several examples.

Noncompliance and instrumental variables for 2k factorial experiments

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Author(s): Matthew Blackwell and Nicole Pashley

Discussant: Teppei Yamamoto (MIT)


Factorial experiments are widely used to assess the marginal, joint, and interactive effects of multiple concurrent factors. While a robust literature covers the design and analysis of these experiments, there is less work on how to handle treatment noncompliance in this setting. To fill this gap, we introduce a new methodology that uses the potential outcomes framework for analyzing 2k factorial experiments with noncompliance on any number of factors. This framework builds on and extends the literature on both instrumental variables and factorial experiments in several ways. First, we define novel, complier-specific quantities of interest for this setting and show how to generalize key instrumental variables assumptions. Second, we show how partial compliance across factors gives researchers a choice over different types of compliers to target in estimation. Third, we show how to conduct inference for these new estimands from both the finite-population and superpopulation asymptotic perspectives. Finally, we illustrate these techniques by applying them to two field experiments—one on the effects of cognitive behavioral therapy on crime and the other on the effectiveness of different forms of get-out-the-vote canvassing. New easy-to-use, open-source software implements the methodology.

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