ml-econometrics - Machine Learning Econometrics for Causal Effects
Applies Post-LASSO, Double/Debiased ML, cross-fitting, and CausalForest methods for high-dimensional controls and treatment-effect inference.
Tags
Updated: 2026-10-05Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Perform Post-LASSO variable selection
- Estimate effects with DoubleML PLR
- Apply cross-fitting for inference
- Estimate heterogeneous effects with CausalForest
- Fit nuisance machine-learning models
Inputs
- Outcome data
- Treatment data
- Control covariates
- Estimator configuration
Outputs
- Treatment-effect estimates
- Standard errors and p-values
- Confidence intervals
- Selected controls
- Fitted estimation results
Requirements
- Python 3.11 environment
- doubleml 0.6 or later
- econml 0.15 or later
- scikit-learn 1.2 or later
- numpy 1.23 or later
- pandas 1.5 or later
- Supported CLI or editor platform