- This looks like a pretty useful paper and - probably more importantly - a pretty useful practical procedure to find our what happens when running Causal Machine Learning. Balancing checks etc are common in traditional approaches (like PSM), but are usually mor tricky to assess in ML-based estimation
- New WP 🚨 1. Recipe to write estimators as weighted outcomes 2. Double ML and causal forests as weighting estimators 3. Plug&play classic covariate balancing checks 4. Explains why Causal ML fails to find an effect of 1 with noiseless outcome Y = 1 + D 5. More fun facts arxiv.org/abs/2411.11559
