Research map / Probabilistic and scientific modeling / Causal discovery
Estimating Identifiable Causal Effects through Double Machine Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DML-ID instead of the plug-in estimator with nuisance models affected by N^(-1/4) converging noise | Estimating P_x(Y=1) in Example 1 · Example 1 simulation | WAAE | Helped |
| Use DML-ID instead of the plug-in estimator with nuisance models affected by N^(-1/4) converging noise | Estimating P_x(Y=1) in Example 2 · Example 2 simulation | WAAE | Helped |
| Use DML-ID instead of the plug-in estimator when {P(x,y|r,w), P(w)} are misspecified | Estimating P_x(Y=1) in Example 1 · Example 1, DR-1 | WAAE | Helped |
| Use DML-ID instead of the plug-in estimator when {P(y|x1,x2,r,z), P(x1,z)} are misspecified | Estimating P_x1,x2(Y=1) in Example 2 · Example 2, DR-1 | WAAE | Helped |
| Use DML-ID instead of the plug-in estimator when {P(r|x1), P(x2|x1,z)} are misspecified | Estimating P_x1,x2(Y=1) in Example 2 · Example 2, DR-2 | WAAE | Helped |
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