Causal inference

A PhD-level course. Practical classes are taught in R and Stata. Participants are asked to install the software before the course.

Software: R and Stata
Contact hours: 24 hours
Previous edition: 13–16 January 2025, daily 15:00–21:30 GMT+1 (Warsaw, Madrid, Berlin, Rome), 9:00–15:30 EST (Boston, New York, Washington)
Next edition: Dates will be announced on the PhD courses page.

Detailed course description

Introduction to causal inference, quick history
Potential outcomes, counterfactuals, basic definitions
RCT, randomization, power calculation, selection bias, heterogeneous treatment bias, DAG notation
Propensity score matching, nearest neighbor covariate matching
Instrumental variables, 2SLS estimation
Weak instruments, just-identified IV, overidentification
Formula IVs
Weighting estimators of the local average treatment effect
Regression discontinuity
Optimal bandwidth selection, robust confidence intervals, local randomization RD analysis, local polynomial order
Quantile treatment effects in the RD
Window selection based on covariates
Spatial RD
Regression discontinuity in time
2×2 difference-in-differences, parallel trends, triple difference
DID with covariates, two-way fixed effects (TWFE)
Nonbinary response (binary, fractional, nonnegative), ratio in ratios, ratio in odds-ratios
Differential timing, Bacon decomposition
Group-time average treatment effects
Imputation estimators, Mundlak estimator, fuzzy difference-in-differences
Synthetic control
Synthetic difference-in-differences
Causal latent variable models

Registration and fees

After registration, each participant receives an email with an invoice and payment details. Discounts and the cancellation policy are described on the payments page. Questions: contact@slawomirkonopa.com.