Causal Inference

252 papers with code • 1 benchmarks • 4 datasets

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

Libraries

Use these libraries to find Causal Inference models and implementations
2 papers
5,105
2 papers
2,375
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420

Most implemented papers

Causal Effect Inference with Deep Latent-Variable Models

AMLab-Amsterdam/CEVAE NeurIPS 2017

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers.

Unbiased Scene Graph Generation from Biased Training

KaihuaTang/Scene-Graph-Benchmark.pytorch CVPR 2020

Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".

Double/Debiased Machine Learning for Treatment and Causal Parameters

Microsoft/EconML 30 Jul 2016

Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.

Adapting Text Embeddings for Causal Inference

blei-lab/causal-text-embeddings 29 May 2019

To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.

DoWhy: An End-to-End Library for Causal Inference

microsoft/dowhy 9 Nov 2020

In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent.

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

sweichwald/coroICA-python 4 Jun 2018

We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.

Interpretable Almost Matching Exactly for Causal Inference

almostExactMatch/collapsingFLAME 18 Jun 2018

Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.

Local Linear Forests

swager/grf 30 Jul 2018

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects.

Using Embeddings to Correct for Unobserved Confounding in Networks

vveitch/causal-network-embeddings NeurIPS 2019

We validate the method with experiments on a semi-synthetic social network dataset.

Deep Reinforcement Learning for Multi-Agent Interaction

uoe-agents/epymarl 2 Aug 2022

The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.