# General targeted machine learning for modern causal mediation analysis

arXiv: https://arxiv.org/abs/2408.14620 · HTML: https://mlsanity.com/paper/2408.14620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 65 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests](https://mlsanity.com/paper/2110.03031/) (2021)
- [De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers](https://mlsanity.com/paper/1802.08667/) (2018)
- [Non-parametric causal effects based on longitudinal modified treatment policies](https://mlsanity.com/paper/2006.01366/) (2020)
- [Sequential Double Robustness in Right-Censored Longitudinal Models](https://mlsanity.com/paper/1705.02459/) (2017)
- [An Interventionist Approach to Mediation Analysis](https://mlsanity.com/paper/2008.06019/) (2020)
- [Non-parametric efficient causal mediation with intermediate confounders](https://mlsanity.com/paper/1912.09936/) (2019)
- [On the multiply robust estimation of the mean of the g-functional](https://mlsanity.com/paper/1705.08582/) (2017)
- [Causal mediation analysis for stochastic interventions](https://mlsanity.com/paper/1901.02776/) (2019)
- [Targeted Maximum Likelihood Based Estimation for Longitudinal Mediation Analysis](https://mlsanity.com/paper/2304.04904/) (2023)
- [Separable Effects for Causal Inference in the Presence of Competing Events](https://mlsanity.com/paper/1901.09472/) (2019)
- [Nonparametric inference for interventional effects with multiple mediators](https://mlsanity.com/paper/2001.06027/) (2020)
- [Nonparametric causal mediation analysis for stochastic interventional (in)direct effects](https://mlsanity.com/paper/2009.06203/) (2020)
- [Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible](https://mlsanity.com/paper/1509.00351/) (2015)
- [Causal survival analysis under competing risks using longitudinal modified treatment policies](https://mlsanity.com/paper/2202.03513/) (2022)
- [On the Causal Interpretation of Randomized Interventional Indirect Effects](https://mlsanity.com/paper/2203.00245/) (2022)
- [Organic direct and indirect effects with post-treatment common causes of mediator and outcome](https://mlsanity.com/paper/1510.02753/) (2015)
- [Causal organic indirect and direct effects: closer to Baron and Kenny, with a product method for binary mediators](https://mlsanity.com/paper/1903.04697/) (2019)
- [Non-agency interventions for causal mediation in the presence of intermediate confounding](https://mlsanity.com/paper/2205.08000/) (2022)
- [Recanting twins: addressing intermediate confounding in mediation analysis](https://mlsanity.com/paper/2401.04450/) (2024)

## Built on by (most cited first, 1 of 1)

- [Proximal Path-Specific Inference](https://mlsanity.com/paper/2605.09462/) (2026)

## Abstract (arXiv)

Causal mediation analyses investigate the mechanisms through which causes exert their effects, and are therefore central to scientific progress. The literature on the non-parametric definition and identification of mediational effects in rigourous causal models has grown significantly in recent years, and there has been important progress to address challenges in the interpretation and identification of such effects. Despite great progress in the causal inference front, statistical methodology for non-parametric estimation has lagged behind, with few or no methods available for tackling non-parametric estimation in the presence of multiple, continuous, or high-dimensional mediators. In this paper we show that the identification formulas for six popular non-parametric approaches to mediation analysis proposed in recent years can be recovered from just two statistical estimands. We leverage this finding to propose an all-purpose one-step estimation algorithm that can be coupled with machine learning in any mediation study that uses any of these six definitions of mediation. The estimators have desirable properties, such as √n-convergence and asymptotic normality. Estimating the first-order correction for the one-step estimator requires estimation of complex density ratios on the potentially high-dimensional mediators, a challenge that is solved using recent advancements in so-called Riesz learning. We illustrate the properties of our methods in a simulation study and illustrate its use on real data to estimate the extent to which pain management practices mediate the total effect of having a chronic pain disorder on opioid use disorder.