# 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={Neural Information Processing Systems}, 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…

## 7 Citations

### Counterfactual (Non-)identifiability of Learned Structural Causal Models

- Computer Science
- 2023

This work warns practitioners about non-identifiability of counterfactual inference from observational data, even in the absence of unobserved confounding and assuming known causal structure, and provides an impossibility result for counterfactuality identifiability for general generation mechanisms with multi-dimensional exogenous variables.

### 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 ScienceArXiv
- 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.

### Deep Counterfactual Estimation with Categorical Background Variables

- Computer ScienceArXiv
- 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.

### 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.

### Causal Graph Discovery from Self and Mutually Exciting Time Series

- Computer ScienceArXiv
- 2023

A generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series while achieving comparable prediction performance to powerful “black-box” models such as XGBoost.

### Counterfactual Analysis in Dynamic Latent State Models

- Computer Science
- 2022

To the best of the knowledge, this work is the first to compute lower and upper bounds on a counterfactual query in a dynamic latent-state model and applies it on a breast cancer case study.

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