Causal Inference
358 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
IHDP
NTPairs
ICLR Database
Most implemented papers
Unbiased Scene Graph Generation from Biased Training
KaihuaTang/Scene-Graph-Benchmark.pytorch
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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
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
Causal Effect Inference with Deep Latent-Variable Models
AMLab-Amsterdam/CEVAE
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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.
Structural Intervention Distance (SID) for Evaluating Causal Graphs
FenTechSolutions/CausalDiscoveryToolbox
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To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
Adapting Text Embeddings for Causal Inference
blei-lab/causal-text-embeddings
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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.
Adapting Neural Networks for the Estimation of Treatment Effects
claudiashi57/dragonnet
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NeurIPS 2019
We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.
DoWhy: An End-to-End Library for Causal Inference
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
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.
Estimating individual treatment effect: generalization bounds and algorithms
clinicalml/cfrnet
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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.
Counterfactual Fairness
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.
