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Counterfactual Inference

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Deep Kalman Filters

Uri Shalit, D. Sontag, R. G. Krishnan•Sun Nov 15 2015

A unified algorithm is introduced to efficiently learn a broad spectrum of Kalman filters and investigates the efficacy of temporal generative models for counterfactual inference, and introduces the "Healing MNIST" dataset where long-term structure, noise and actions are applied to sequences of digits.

395

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Deep Structural Causal Models for Tractable Counterfactual Inference

Daniel Coelho de Castro, Nick Pawlowski, B. Glocker•Wed Jun 10 2020

The experimental results indicate that the proposed framework can successfully train deep SCMs that are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals, giving rise to a powerful new approach for answering causal questions in imaging applications and beyond.

288 0

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SurvCaus : Representation Balancing for Survival Causal Inference

Ayoub Abraich, Agathe Guilloux, Blaise Hanczar•Mon Mar 28 2022

This paper proposes theoretical guarantees for a representation balancing framework applied to counterfactual inference in a survival setting using a neural network capable of predicting the factual andcounterfactual survival functions (and then the CATE), in the presence of censorship, at the individual level.

1 0

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Learning Representations for Counterfactual Inference

Fredrik D. Johansson, Uri Shalit, D. Sontag•Wed May 11 2016

A new algorithmic framework for counterfactual inference is proposed which brings together ideas from domain adaptation and representation learning and significantly outperforms the previous state-of-the-art approaches.

790 0

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Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks

P. Schwab, Lorenz Linhardt, W. Karlen•Wed Sep 26 2018

Perfect Match is presented, a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments.

119 0

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Counterfactual inference for consumer choice across many product categories

S. Athey, D. Blei, Rob Donnelly, Francisco J. R. Ruiz•Wed Jun 05 2019

This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel, and shows that the model improves over traditional modeling approaches that consider each category in isolation.

37 0

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MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

Jonathan G. Richens, Ciarán M. Lee, Saurabh Johri, Yura N. Perov, L. Graham, Kostis Gourgoulias, Adam Baker•Wed Oct 16 2019

By considering the structure of the counterfactual query, one can significantly optimise the inference process, and MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning, is introduced.

18 0

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Counterfactual Inference | State-of-the-Art