Causal Inference
Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
State-of-the-art leaderboards
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Greatest papers with code
Orthogonal Random Forest for Causal Inference
We show that under mild assumption on the consistency rate of the nuisance estimator, we can achieve the same error rate as an oracle with a priori knowledge of these nuisance parameters.
Double/Debiased Machine Learning for Treatment and Causal Parameters
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
Structural Intervention Distance (SID) for Evaluating Causal Graphs
• Diviyan-Kalainathan/CausalDiscoveryToolbox
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To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
RankPL: A Qualitative Probabilistic Programming Language
In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn's ranking theory.
Estimating individual treatment effect: generalization bounds and algorithms
ICML 2017
• clinicalml/cfrnet
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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.
SOTA for Causal Inference on IDHP
Using Text Embeddings for Causal Inference
• blei-lab/causal-text-embeddings
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A key insight is that causal adjustment requires only the aspects of text that are predictive of both the treatment and outcome.
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
• IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework
Causal inference analysis is the estimation of the effects of actions on outcomes.
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
• orcax/PGPR
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To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph.
Adapting Neural Networks for the Estimation of Treatment Effects
We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.
Representation Learning for Treatment Effect Estimation from Observational Data
NeurIPS 2018
• Osier-Yi/SITE
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Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.