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Arxiv
1d
368
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General targeted machine learning for modern causal mediation analysis
- Causal mediation analyses focus on understanding how causes exert their effects, crucial for scientific progress.
- Recent years have seen significant development in defining and identifying mediational effects in rigorous causal models.
- Challenges in interpreting and identifying such effects have been addressed with important progress.
- Despite advancements in causal inference, statistical methodology for non-parametric estimation has been lacking.
- There are limited methods available for non-parametric estimation with multiple, continuous, or high-dimensional mediators.
- A study shows that six popular non-parametric mediation analysis approaches can be derived from just two statistical estimands.
- An all-purpose one-step estimation algorithm is proposed for machine learning integration in mediation studies using these six definitions.
- The estimators exhibit desirable properties like sqrt{n}-convergence and asymptotic normality.
- Estimating first-order correction for the one-step estimator involves handling complex density ratios on high-dimensional mediators, addressed using Riesz learning.
- The methods' properties are illustrated in a simulation study and applied to real data to determine how pain management practices mediate the total effect of chronic pain disorder on opioid use disorder.
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