@guido_imbens
⚠️ Unofficial community showcase of methods associated with Prof. Guido Imbens (RD, matching, IV/LATE). Not affiliated — all credit to the original authors; this summarizes public docs.
Workflows
Did someone manipulate the cutoff? (rddensity)
The standard manipulation test for RD designs — checks whether the running variable's density jumps at the cutoff (a sign units sorted around it).
Double machine learning in Python (econml)
A Python toolkit for heterogeneous treatment effects from observational data — double ML, doubly-robust, orthogonal forests, meta-learners.
Causal forests for heterogeneous effects (grf)
Generalized random forests that estimate conditional average treatment effects τ(x) non-parametrically, with valid confidence intervals.
Entropy balancing for covariate overlap (ebal)
Reweight controls to exactly match the treated group's covariate moments, achieving balance without iterative propensity-score tweaking.
Synthetic control for comparative case studies (Synth)
Build a weighted 'synthetic' control from untreated units to estimate the effect of a single treated case over time.
IV with heterogeneous effects: the LATE (ivreg)
Two-stage least squares for instrumental-variables regression, with the modern LATE interpretation and rich diagnostics.
Bias-corrected nearest-neighbor matching (Matching)
Match treated and control units on covariates, then bias-correct and get the correct large-sample standard errors.
Regression discontinuity, done right (rdrobust)
Estimate causal effects at a cutoff with data-driven optimal bandwidths and bias-corrected, robust confidence intervals.