Skip to content
#

conditional-average-treatment-effects

Star

Here are 2 public repositories matching this topic...

Language: All
Filter by language
All 2 Jupyter Notebook 1 R 1

HAYDARKILIC / causal_inference_for_ai

Star 0

A research-grade, 6-week masterclass in Causal Inference and Causal ML from first principles. Rebuilds d-separation oracles, propensity score IRLS engines, doubly-robust AIPW estimators, Cross-Fitting Double Machine Learning (DML), and honest causal forests from scratch in pure NumPy. Fully verified against causal truth

python dml artificial-intelligence causal-inference instrumental-variables dags cate double-machine-learning meta-learners directed-acyclic-graphs conditional-average-treatment-effects inverse-probability-weighting causal-forests potential-outcomes selection-bias-decomposition neyman-orthogonality randomization-mechanics back-door-criterion front-door-criterion regression-discontinuity-design
  • Updated May 30, 2026
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the conditional-average-treatment-effects topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the conditional-average-treatment-effects topic, visit your repo's landing page and select "manage topics."

Learn more