Why did the distribution change?

Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021.

Abstract

We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables. After factorizing the joint distribution into conditional distributions of each variable, given its parents (the "causal mechanisms"), we attribute the change to changes of these causal mechanisms. This attribution analysis accounts for the fact that mechanisms often change independently and sometimes only some of them change. Through simulations, we study the performance of our distribution change attribution proposal. We then present a real-world case study identifying the drivers of the difference in the income distribution between men and women.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-budhathoki21a, title = { Why did the distribution change? }, author = {Budhathoki, Kailash and Janzing, Dominik and Bloebaum, Patrick and Ng, Hoiyi}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {1666--1674}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/budhathoki21a/budhathoki21a.pdf}, url = {https://proceedings.mlr.press/v130/budhathoki21a.html}, abstract = { We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables. After factorizing the joint distribution into conditional distributions of each variable, given its parents (the "causal mechanisms"), we attribute the change to changes of these causal mechanisms. This attribution analysis accounts for the fact that mechanisms often change independently and sometimes only some of them change. Through simulations, we study the performance of our distribution change attribution proposal. We then present a real-world case study identifying the drivers of the difference in the income distribution between men and women. } }
Endnote
%0 Conference Paper %T Why did the distribution change? %A Kailash Budhathoki %A Dominik Janzing %A Patrick Bloebaum %A Hoiyi Ng %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-budhathoki21a %I PMLR %P 1666--1674 %U https://proceedings.mlr.press/v130/budhathoki21a.html %V 130 %X We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables. After factorizing the joint distribution into conditional distributions of each variable, given its parents (the "causal mechanisms"), we attribute the change to changes of these causal mechanisms. This attribution analysis accounts for the fact that mechanisms often change independently and sometimes only some of them change. Through simulations, we study the performance of our distribution change attribution proposal. We then present a real-world case study identifying the drivers of the difference in the income distribution between men and women.
APA
Budhathoki, K., Janzing, D., Bloebaum, P. & Ng, H.. (2021). Why did the distribution change? . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:1666-1674 Available from https://proceedings.mlr.press/v130/budhathoki21a.html.

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