Counterfactual Inference

37 papers with code • 0 benchmarks • 2 datasets

This task has no description! Would you like to contribute one?

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.

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 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.

Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems

kaushalpaneri/ode2scm NeurIPS 2019

In contrast, structural causal models support counterfactual inference, but do not identify the mechanisms.

Counterfactual VQA: A Cause-Effect Look at Language Bias

yuleiniu/cfvqa CVPR 2021

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language.

Learning Decomposed Representation for Counterfactual Inference

anpwu/der-cfr 12 Jun 2020

The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing.