# Distributional Causal Mediation via Conditional Generative Modeling

arXiv: https://arxiv.org/abs/2605.01765 · HTML: https://mlsanity.com/paper/2605.01765/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 14 of 14)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Engression: Extrapolation through the Lens of Distributional Regression](https://mlsanity.com/paper/2307.00835/) (2023)
- [Semiparametric counterfactual density estimation](https://mlsanity.com/paper/2102.12034/) (2021)
- [Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression](https://mlsanity.com/paper/2102.08208/) (2021)
- [Robust and Agnostic Learning of Conditional Distributional Treatment Effects](https://mlsanity.com/paper/2205.11486/) (2022)
- [Causal effects based on distributional distances](https://mlsanity.com/paper/1806.02935/) (2018)
- [Wasserstein Generative Regression](https://mlsanity.com/paper/2306.15163/) (2023)
- [On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization](https://mlsanity.com/paper/2506.05945/) (2025)
- [Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn](https://mlsanity.com/paper/1904.08515/) (2019)
- [Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves](https://mlsanity.com/paper/2111.03950/) (2021)
- [Nonparametric inference for interventional effects with multiple mediators](https://mlsanity.com/paper/2001.06027/) (2020)
- [Non-linear Mediation Analysis with High-dimensional Mediators whose Causal Structure is Unknown](https://mlsanity.com/paper/2001.07147/) (2020)
- [Causal Mediation Analysis with Multiple Mediators: A Simulation Approach](https://mlsanity.com/paper/2506.14019/) (2025)

## Abstract (arXiv)

Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional changes induced by complex and nonlinear causal mechanisms. We propose Distributional Causal Mediation Analysis (DCMA), a generative learning framework for identifying and estimating treatment effects on entire outcome distributions transmitted through multiple mediators. DCMA learns conditional generative models for the mediators and the outcome, recovering the relevant conditional distributions from observational data. Leveraging the identification formulas, it reconstructs interventional outcome distributions via Monte Carlo forward simulation by noise resampling, enabling the capture of both classical summary effects and rich distributional contrasts such as energy distance and the Wasserstein distance. Analytical error bounds are derived to decompose how estimation errors in the learned conditional models propagate to the reconstructed interventional outcome distributions. The empirical effectiveness of DCMA is demonstrated through numerical experiments and real-world data applications.