In an analysis of Medicare data, patients hospitalized with acute myocardial infarction 2 weeks after, as compared with 2 weeks before, their 80th birthday were significantly less likely to undergo CABG. This appears to be an example of left-digit bias, which is a behavioral heuristic (a mental shortcut that simplifies decision making).
Selected Work in Progress
Estimating the Counterfactual Life Spans of COVID-19 Decedents: Evidence from New York City
Worth the Price of Admission? Evidence from Emergency Department Admissions
Incorporating Compliance Prediction into RCT Design
Instrumental variables (IV) regression is widely used to estimate causal treatment effects in settings where receipt of treatment is not fully random, but there exists an instrument that generates exogenous variation in treatment exposure. While IV can recover consistent treatment effect estimates, they are often noisy. Building upon earlier work in biostatistics (Joffe and Brensinger, 2003) and relating to an evolving literature in econometrics (including Abadie et al., 2019; Huntington-Klein, 2020; Borusyak and Hull, 2020), we study how to improve the efficiency of IV estimates by exploiting the predictable variation in the strength of the instrument. In the case where both the treatment and instrument are binary and the instrument is independent of baseline covariates, we study weighting each observation according to its estimated compliance (that is, its conditional probability of being affected by the instrument), which we motivate from a (constrained) solution of the first-stage prediction problem implicit to IV. The resulting estimator can leverage machine learning to estimate compliance as a function of baseline covariates. We derive the large-sample properties of a specific implementation of a weighted IV estimator in the potential outcomes and local average treatment effect (LATE) frameworks, and provide tools for inference that remain valid even when the weights are estimated nonparametrically. With both theoretical results and a simulation study, we demonstrate that compliance weighting meaningfully reduces the variance of IV estimates when first-stage heterogeneity is present, and that this improvement often outweighs any difference between the compliance-weighted and unweighted IV estimands. These results suggest that in a variety of applied settings, the precision of IV estimates can be substantially improved by incorporating compliance estimation.
This paper explores the use of heuristics among highly-trained physicians diagnosing heart disease in the emergency department, a common task with life-or-death consequences. Using data from a large private-payer claims database, I find compelling evidence of heuristic thinking in this setting: patients arriving in the emergency department just after their 40th birthday are roughly 10% more likely to be tested for and 20% more likely to be diagnosed with ischemic heart disease (IHD) than patients arriving just before this date, despite the fact that the incidence of heart disease increases smoothly with age. Moreover, I show that this shock to diagnostic intensity has meaningful implications for patient health, as it reduces the number of missed IHD diagnoses among patients arriving in the emergency department just after their 40th birthday, thereby preventing future heart attacks. I then develop a model that ties this behavior to an existing literature on representativeness heuristics, and discuss the implications of this class of heuristics for diagnostic decision making.