Counterfactual Inference

49 papers with code • 0 benchmarks • 2 datasets

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Most implemented papers

Deep Kalman Filters

clinicalml/structuredinference 16 Nov 2015

Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.

Deep Structural Causal Models for Tractable Counterfactual Inference

biomedia-mira/deepscm NeurIPS 2020

We formulate a general framework for building structural causal models (SCMs) with deep learning components.

SurvCaus : Representation Balancing for Survival Causal Inference

abraich/pycaus 29 Mar 2022

Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.

Interventional and Counterfactual Inference with Diffusion Models

patrickrchao/diffusionbasedcausalmodels 2 Feb 2023

We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available.

Learning Representations for Counterfactual Inference

sschrod/adbcr 12 May 2016

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology.

RNN-based counterfactual prediction, with an application to homestead policy and public schooling

jvpoulos/rnns-causal 10 Dec 2017

This paper proposes a method for estimating the effect of a policy intervention on an outcome over time.

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

d909b/perfect_match ICLR 2019

However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.

Counterfactual Inference for Consumer Choice Across Many Product Categories

rodonn/nested-factorization 6 Jun 2019

One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data.

Counterfactual diagnosis

babylonhealth/counterfactual-diagnosis 15 Oct 2019

We show that this approach is closer to the diagnostic reasoning of clinicians and significantly improves the accuracy and safety of the resulting diagnoses.

MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

babylonhealth/multiverse pproximateinference AABI Symposium 2019

We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.