Sensitivity to parametric assumptions, revealing inferences too far from the data to have empirical answers, the curse of dimensionality, extrapolation, measures of distance from the data.
Matching methods as nonparametric preprocessing to reduce model dependence in parametric causal inference.
Intro to Matching and Review of Model Dependence (06:02)
Matching as Solution to Model Dependence (27:23)
Matching and Causal Quantities of Interest (07:44)
3 Methods of Matching (25:22)
Space Graphs: Visualizing Bias-Variance Tradeoff in Matching (17:30)
Reorientation on Matching Methods and Space Graphs (16:03)
Propensity Score Matching as Approximating Random Matching (15:57)
Problems with Popular Matching Methods and CEM as a Valuable Alternative (11:46)
An overview of how key features of various observational and experimental research designs, and the designs themselves, reduce components of error in estimating causal effects.