Methods Catalog
From established workhorse regressions to frontier machine learning approaches for causal inference.
Showing 27 of 27 methods
Experimental Design
The gold standard for internal validity — random assignment eliminates selection bias by design.
OLS (Robust SEs, Clustering)
The workhorse of empirical research — linear regression with modern standard error corrections.
Logit / Probit
Models for binary outcomes — when your dependent variable is yes/no, pass/fail, or adopt/don't adopt.
Poisson / Negative Binomial
Models for count outcomes — patents filed, citations received, number of acquisitions.
Cox Proportional Hazard Model
Models the hazard rate of an event (failure, exit, adoption) as a function of covariates, using a semiparametric baseline hazard that does not require distributional assumptions.
Fixed Effects (Two-Way FE)
Removes time-invariant unobserved confounders by exploiting within-unit variation over time.
Random Effects
A more efficient alternative to fixed effects when the unobserved effect is uncorrelated with regressors.
Difference-in-Differences (Canonical 2×2)
Estimates causal effects by comparing changes over time between treated and control groups.
Interrupted Time Series (ITS)
Estimates causal effects of interventions by modeling level and slope changes in a single unit's time series at the intervention point.
Regression Discontinuity Design – Sharp
Exploits a sharp cutoff in treatment assignment to estimate causal effects near the threshold.
Regression Kink Design (RKD)
Identifies causal effects from a kink (slope change) in the treatment assignment function, estimating a ratio of derivatives rather than a level discontinuity.
Regression Discontinuity Design – Fuzzy
When crossing the cutoff changes the probability of treatment (not a guarantee), use fuzzy RDD — essentially IV at the cutoff.
Matching (PSM, CEM, NN, Weighting)
Reduces selection bias by comparing treated units to similar control units based on observed characteristics.
Heckman Selection Model
Corrects for sample selection bias when the outcome is observed only for a non-random subset of the population, using a two-equation system with an exclusion restriction.
Instrumental Variables / 2SLS
Uses an external source of variation (instrument) that affects treatment but not the outcome directly.
Event Studies (Dynamic Treatment Effects)
Visualize how treatment effects evolve over time — and test whether pre-trends support the parallel trends assumption.
Staggered DiD
Under staggered adoption with heterogeneous effects, traditional TWFE can produce biased estimates — modern estimators correct for this.
Synthetic Control
Constructs a weighted combination of control units that best approximates the treated unit's pre-treatment trajectory.
Shift-Share / Bartik Instruments
Uses national-level shocks interacted with local-level exposure to construct instruments for endogenous variables.
Bunching Estimation
Identifies behavioral responses from excess mass at notch or kink points in budget sets, estimating elasticities from distributional distortions.
Doubly Robust / AIPW Estimation
Combines outcome modeling and propensity score weighting — consistent if either model is correctly specified.
Quantile Treatment Effects (QTE)
Estimates how treatment shifts the entire outcome distribution, revealing heterogeneous effects across quantiles that average effects conceal.
Causal Mediation Analysis
Goes beyond 'does the treatment work?' to ask 'through which pathway does it work?' — extends the Baron-Kenny framework by addressing its identification challenges.
Synthetic Difference-in-Differences
Combines the strengths of DiD (parallel trends) and synthetic control (matching on pre-treatment trajectory) into a single estimator.
Double/Debiased Machine Learning (DML)
Uses machine learning for nuisance parameter estimation while preserving valid inference on the causal parameter of interest.
Causal Forests / Heterogeneous Treatment Effects
Estimates how treatment effects vary across individuals — who benefits most and who benefits least.
Marginal Treatment Effects (MTE)
Unifies IV/LATE, ATE, and ATT as weighted averages of the MTE curve -- the treatment effect as a function of unobserved resistance to treatment.