Causal Methods
Time-series causal inference and statistical validation methods.
Applied Causal Inference
in progressDual-language causal methods codebase with tests and validation architecture. Implements DoubleML, instrumental variables, and time-series-aware variants in both Julia and Python. The intent is reference-quality implementations with explicit validation against synthetic data and known answers.
Stack: Julia · Python · DoubleML · CausalImpact
What's next
Currently dormant; reference-quality state. Future direction: add additional estimators and integrate with the temporal-validation utilities.
double_ml_time_series
in progressImplementation and methodology notes for double machine learning extended to time-series settings. Covers temporal partially-linear DML, cross-fitting protocols that respect time ordering, and HAC-style inference. Synthetic examples illustrate where naive cross-fitting fails and what to do instead.
Stack: Python · DoubleML · statsmodels · LaTeX
What's next
Web edition is porting from the LaTeX manuscript — Chapter 1 (potential outcomes + Frisch–Waugh–Lovell) is live; remaining chapters in progress. The full 10-chapter manuscript ships as a PDF in the repo.
TemporalValidation / temporalcv
releasedUtilities for temporally-aware cross-validation: walk-forward splits with explicit gaps, purging adjacent observations, embargo windows, and other leakage-prevention patterns. The methodology lineage notes connect classical temporal CV practice to modern uses in causal and ML pipelines.
Stack: Julia · Python
What's next
Reference-quality state. Future: package as installable Python/Julia modules with consistent API.