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
Worth the Price of Admission? Evidence from Emergency Department Admissions
Incorporating Compliance Prediction into RCT Design
Estimating the Counterfactual Life Spans of COVID-19 Decedents: Evidence from New York City
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.
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. Relating to an evolving literature spanning biostatistics and econometrics (Joffe and Brensinger, 2003; Abadie et al., 2019; Huntington-Klein, 2020; Borusyak and Hull, 2020), we study how to use covariate information to improve the precision of IV estimates in the common scenario in which both the treatment and instrument are binary, which we view through the lenses of potential outcome and local average treatment effect (LATE) frameworks. The efficient use of covariates in this setting involves nonparametrically solving a causal heterogeneous treatment effect estimation problem within the first stage of IV: identifying the conditional average treatment effects of the instrument on treatment (i.e., the conditional probabilities of compliance). We derive the large-sample properties of a compliance-weighted IV estimator, and provide procedures for conducting valid inference when using estimated compliance weights, including weights that are estimated nonparametrically with machine learning methods. 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 these improvements often dwarf the variation in the estimand relative to a standard two-stage least-squares regression. These results suggest that in a variety of applied settings, the precision of IV estimates can be substantially improved by incorporating compliance estimation.