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.
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.
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).
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
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.
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.
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.
Challenges of Using Text Classifiers for Causal Inference
EMNLP 2018 • zachwooddoughty/emnlp2018-causal
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets.
Ancestral Causal Inference
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions.