Causal Inference

463 papers with code • 3 benchmarks • 8 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
6,915
2 papers
3,674
2 papers
693
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Most implemented papers

Causal Effect Inference with Deep Latent-Variable Models

uber/causalml 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".

Adapting Neural Networks for the Estimation of Treatment Effects

claudiashi57/dragonnet NeurIPS 2019

We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.

Structural Intervention Distance (SID) for Evaluating Causal Graphs

FenTechSolutions/CausalDiscoveryToolbox 5 Jun 2013

To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).

Estimating individual treatment effect: generalization bounds and algorithms

clinicalml/cfrnet ICML 2017

We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation.

Double/Debiased Machine Learning for Treatment and Causal Parameters

py-why/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.

BART: Bayesian additive regression trees

JakeColtman/bartpy 19 Jun 2008

We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior.

Counterfactual Fairness

mkusner/counterfactual-fairness NeurIPS 2017

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.