# Multiply-Robust Causal Change Attribution

arXiv: https://arxiv.org/abs/2404.08839 · HTML: https://mlsanity.com/paper/2404.08839/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Algorithms to estimate Shapley value feature attributions](https://mlsanity.com/paper/2207.07605/) (2022)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [Causal structure based root cause analysis of outliers](https://mlsanity.com/paper/1912.02724/) (2019)
- [DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models](https://mlsanity.com/paper/2206.06821/) (2022)
- [Approximating the Shapley Value without Marginal Contributions](https://mlsanity.com/paper/2302.00736/) (2023)
- [Program Evaluation and Causal Inference with High-Dimensional Data](https://mlsanity.com/paper/1311.2645/) (2013)
- [Towards Explaining Distribution Shifts](https://mlsanity.com/paper/2210.10275/) (2022)
- [Long Story Short: Omitted Variable Bias in Causal Machine Learning](https://mlsanity.com/paper/2112.13398/) (2021)
- [Permutation Weighting](https://mlsanity.com/paper/1901.01230/) (2019)
- [Quantifying intrinsic causal contributions via structure preserving interventions](https://mlsanity.com/paper/2007.00714/) (2020)
- [The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages](https://mlsanity.com/paper/1512.05635/) (2015)
- [The Counterfactual-Shapley Value: Attributing Change in System Metrics](https://mlsanity.com/paper/2208.08399/) (2022)
- [Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK](https://mlsanity.com/paper/1811.11603/) (2018)

## Built on by (most cited first, 3 of 3)

- ["Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift](https://mlsanity.com/paper/2506.00756/) (2025)
- [Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition](https://mlsanity.com/paper/2603.29972/) (2026)
- [SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets](https://mlsanity.com/paper/2605.00944/) (2026)

## Abstract (arXiv)

Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy that, given a causal model, combines regression and re-weighting methods to quantify the contribution of each causal mechanism. Our proposed methodology is multiply robust, meaning that it still recovers the target parameter under partial misspecification. We prove that our estimator is consistent and asymptotically normal. Moreover, it can be incorporated into existing frameworks for causal attribution, such as Shapley values, which will inherit the consistency and large-sample distribution properties. Our method demonstrates excellent performance in Monte Carlo simulations, and we show its usefulness in an empirical application. Our method is implemented as part of the Python library DoWhy (arXiv:2011.04216, arXiv:2206.06821).