Efficient Least Squares For Estimating Total Effects Under Linearity And Causal Sufficiency

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" summary

Proposes a reparameterization of linear SCMs over an MP-DAG into ordered bucket blocks and defines a plug‑in G regression estimator that estimates blockwise lambda matrices by least squares. Claims asymptotic dominance: any differentiable, covariance‑based consistent estimator has equal or larger asymptotic variance than G regression (Gaussian errors give MLE equivalence). Simulations show lower mean squared error than optimal adjustment, modified Cholesky and recursive regressions, and G regression still identifies some effects those methods cannot.

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" chapters (10)

1. 0:00Introduction
2. 2:50Running Example
3. 6:05Defining Causal Effects
4. 14:42Causal Graph
5. 16:07Previous Results
6. 18:07Empty Deck
7. 18:42Buckets
8. 26:32G Formula
9. 27:32Simulation
10. 28:34Comparison

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