# Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs

arXiv: https://arxiv.org/abs/1302.1566 · HTML: https://mlsanity.com/paper/1302.1566/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2013.

## Built on by (most cited first, 6 of 6)

- [On Testability of the Front-Door Model via Verma Constraints](https://mlsanity.com/paper/2203.00161/) (2022)
- [On Testability and Goodness of Fit Tests in Missing Data Models](https://mlsanity.com/paper/2203.00132/) (2022)
- [A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment](https://mlsanity.com/paper/2111.13226/) (2021)
- [Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data](https://mlsanity.com/paper/2410.21531/) (2024)
- [A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding](https://mlsanity.com/paper/2402.02306/) (2024)
- [Robust Weighted Triangulation of Causal Effects Under Model Uncertainty](https://mlsanity.com/paper/2603.01119/) (2026)

## Abstract (arXiv)

The standard way to parameterize the distributions represented by a directed acyclic graph is to insert a parametric family for the conditional distribution of each random variable given its parents. We show that when one's goal is to test for or estimate an effect of a sequentially applied treatment, this natural parameterization has serious deficiencies. By reparameterizing the graph using structural nested models, these deficiencies can be avoided.