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Causal Inference with Noisy and Missing Covariates via Matrix Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use matrix-factorization-inferred confounders instead of noisy covariates | Linear regression ATE estimation · Synthetic data, high-dimensional setting | relative RMSE | Helped |
| Use matrix factorization-inferred confounders instead of noisy covariates | Causal inference methods including LR, DR, matching, and propensity-score matching · Twins mortality, complete data | ATE estimation error | Helped |
| Use matrix factorization to infer confounders rather than impute noisy covariates | Logistic regression preprocessing · Twins mortality, missing data | ATE estimation error | Helped |
| Increase the number of noisy covariates | ATE estimators using noisy covariates · Synthetic data | relative RMSE | Helped |
| Use matrix factorization preprocessing rather than direct OLS on noisy covariates | Linear regression ATE estimation · Synthetic data, Gaussian covariates with additive noise | relative RMSE | Mixed |
| Use a data-type-adapted matrix-factorization loss for binary covariates | Matrix-factorization preprocessing for ATE estimation · Synthetic data, binary covariates | relative RMSE | Helped |
| Use matrix-factorization-inferred confounders instead of noisy covariates | Linear regression ATE estimation · Synthetic data with few covariates | relative RMSE | Hurt |
| Keep the number of covariates fixed while increasing sample size | ATE estimation with measurement noise · Synthetic data | ATE estimation error | Hurt |
| Use matrix factorization preprocessing with incomplete covariates | Logistic regression ATE estimation · Twins mortality, missing data | ATE estimation error | Helped |
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