Position: Probabilistic Modelling is Sufficient for Causal Inference

Bruno Kacper Mlodozeniec, David Krueger, Richard E Turner
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81810-81840, 2025.

Abstract

Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we make it clear that you can answer any causal inference question within the realm of probabilistic modelling and inference, without causal-specific tools or notation. Through concrete examples, we demonstrate how causal questions can be tackled by writing down the probability of everything. We argue for the advantages of the generality of the probabilistic modelling lens, when compared to bespoke causal frameworks. Lastly, we reinterpret causal tools as emerging from standard probabilistic modelling and inference, elucidating their necessity and utility.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-mlodozeniec25a, title = {Position: Probabilistic Modelling is Sufficient for Causal Inference}, author = {Mlodozeniec, Bruno Kacper and Krueger, David and Turner, Richard E}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81810--81840}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/mlodozeniec25a/mlodozeniec25a.pdf}, url = {https://proceedings.mlr.press/v267/mlodozeniec25a.html}, abstract = {Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we make it clear that you can answer any causal inference question within the realm of probabilistic modelling and inference, without causal-specific tools or notation. Through concrete examples, we demonstrate how causal questions can be tackled by writing down the probability of everything. We argue for the advantages of the generality of the probabilistic modelling lens, when compared to bespoke causal frameworks. Lastly, we reinterpret causal tools as emerging from standard probabilistic modelling and inference, elucidating their necessity and utility.} }
Endnote
%0 Conference Paper %T Position: Probabilistic Modelling is Sufficient for Causal Inference %A Bruno Kacper Mlodozeniec %A David Krueger %A Richard E Turner %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-mlodozeniec25a %I PMLR %P 81810--81840 %U https://proceedings.mlr.press/v267/mlodozeniec25a.html %V 267 %X Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we make it clear that you can answer any causal inference question within the realm of probabilistic modelling and inference, without causal-specific tools or notation. Through concrete examples, we demonstrate how causal questions can be tackled by writing down the probability of everything. We argue for the advantages of the generality of the probabilistic modelling lens, when compared to bespoke causal frameworks. Lastly, we reinterpret causal tools as emerging from standard probabilistic modelling and inference, elucidating their necessity and utility.
APA
Mlodozeniec, B.K., Krueger, D. & Turner, R.E.. (2025). Position: Probabilistic Modelling is Sufficient for Causal Inference. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81810-81840 Available from https://proceedings.mlr.press/v267/mlodozeniec25a.html.

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