5 papers · 1 filter
What makes a study design quasi-experimental? The case of difference-in-differences
Audrey Renson, Daniel Westreich
Study designs classified as quasi- or natural experiments are typically accorded more face validity than observational study designs more broadly. However, there is ambiguity in th…
Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition
Rachael K. Ross, Lina M. Montoya, Dana E. Goin +2
Use of machine learning to estimate nuisance functions (e.g. outcomes models, propensity score models) in estimators used in causal inference is increasingly common, as it can miti…
Pulling back the curtain: the road from statistical estimand to machine-learning based estimator for epidemiologists (no wizard required)
Audrey Renson, Lina Montoya, Dana E. Goin +2
Epidemiologists increasingly use causal inference methods that rely on machine learning, as these approaches can relax unnecessary model specification assumptions. While deriving a…
Efficient estimation of longitudinal treatment effects using difference-in-differences and machine learning
Nicholas Illenberger, Iván DÃaz, Audrey Renson
Difference-in-differences is based on a parallel trends assumption, which states that changes over time in average potential outcomes are independent of treatment assignment, possi…
Transporting treatment effects from difference-in-differences studies
Audrey Renson, Ellicott C. Matthay, Kara E. Rudolph
Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample…