Which econometric method should you use for causal inference of health policy?

TL;DR A paper by Ress and Wild (2024) provide the following recommendations in answering this question. When aiming to control for a large covariate set, consider using the superlearner to estimate nuisance parameters. When employing the superlearner to estimate nuisance parameters, consider using doubly robust estimation approaches, such as AIPW and TMLE. When faced with…

Impact of mental health on food security

How do mental health issues impact the likelihood of food security? This question is difficult to answer empirically for (at a minimum) two primary reasons: Endogeneity/Unobserved factors. For instance, personal, family, and neighborhood characteristics (e.g., family stability, access to health care, exposure to violence) may impact both mental health and the likelihood of food insecurity.…

Should you adjust for covariates when analyzing data from randomized controlled trials?

FDA draft guidance published this month says you should. In most cases, adjusting for covariates is not necessary. Randomization generally insurers that covariates are balanced across clinical trial arms. Randomization, however, may not always result in perfectly balanced trial arms. In these cases, the FDA notes that covariate adjustment is perfectly acceptable. There are some…

Two‐Stage Residual Inclusion: An Overview

Often times, researchers want to measure the effect of certain interventions in the real-world. Doing this in practice is often difficult.  For instance, consider measuring health outcomes among individuals who visit doctors compared to those who don’t.  Inevitably, individuals who visit doctors will have worse outcomes.  Why?  Are doctors killing patients?   This is clearly a…