Causal Inference
425 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
Causal Effect Inference with Deep Latent-Variable Models
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
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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".
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.
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).
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.
Double/Debiased Machine Learning for Treatment and Causal Parameters
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
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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.
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.
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.
