# Learning Generalized Gumbel-max Causal Mechanisms

@inproceedings{Lorberbom2021LearningGG, title={Learning Generalized Gumbel-max Causal Mechanisms}, author={Guy Lorberbom and Daniel D. Johnson and Chris J. Maddison and Daniel Tarlow and Tamir Hazan}, booktitle={NeurIPS}, year={2021} }

To perform counterfactual reasoning in Structural Causal Models (SCMs), one needs to know the causal mechanisms, which provide factorizations of conditional distributions into noise sources and deterministic functions mapping realizations of noise to samples. Unfortunately, the causal mechanism is not uniquely identified by data that can be gathered by observing and interacting with the world, so there remains the question of how to choose causal mechanisms. In recent work, Oberst & Sontag…

## 4 Citations

### Estimating Categorical Counterfactuals via Deep Twin Networks

- Computer Science
- 2021

This work introduces the notion of counterfactual ordering, a principle that posits desirable properties causal mechanisms should posses, and proves that it is equivalent to speciﬁc functional constraints on the causal mechanisms.

### Counterfactual Analysis in Dynamic Models: Copulas and Bounds

- Computer Science
- 2022

The entire space of SCMs obeying counterfactual stability (CS) is characterized, and it is used to negatively answer the open question of Oberst and Sontag regarding the uniqueness of the Gumbel-max mechanism for modeling CS.

### Counterfactual Inference of Second Opinions

- Computer ScienceUAI
- 2022

A set invariant Gumbel-Max structural causal model is designed where the structure of the noise governing the sub-mechanisms underpinning the model depends on an intuitive notion of similarity between experts which can be estimated from data.

### Deep Counterfactual Estimation with Categorical Background Variables

- Computer Science
- 2022

This work introduces CounterFactual Query Prediction (CFQP), a novel method to infer counterfactuals from continuous observations when the background variables are categorical, and shows that the method signiﬁcantly outperforms previously available deep-learning-basedcounterfactual methods, both theoretically and empirically on time series and image data.

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