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Causal Inference

23 papers with code · Miscellaneous

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

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Greatest papers with code

Orthogonal Random Forest for Causal Inference

9 Jun 2018Microsoft/EconML

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.

CAUSAL INFERENCE

Double/Debiased Machine Learning for Treatment and Causal Parameters

30 Jul 2016Microsoft/EconML

Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.

CAUSAL INFERENCE

RankPL: A Qualitative Probabilistic Programming Language

19 May 2017tjitze/RankPL

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.

CAUSAL INFERENCE PROBABILISTIC PROGRAMMING

Estimating individual treatment effect: generalization bounds and algorithms

ICML 2017 clinicalml/cfrnet

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.

CAUSAL INFERENCE

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.

CAUSAL INFERENCE DECISION MAKING

Ancestral Causal Inference

NeurIPS 2016 caus-am/aci

Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions.

CAUSAL DISCOVERY CAUSAL INFERENCE

Almost-Exact Matching with Replacement for Causal Inference

18 Jun 2018almostExactMatch/collapsingFLAME

Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.

CAUSAL INFERENCE

Representation Learning for Treatment Effect Estimation from Observational Data

NeurIPS 2018 Osier-Yi/SITE

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.

CAUSAL INFERENCE REPRESENTATION LEARNING

On Adaptive Propensity Score Truncation in Causal Inference

18 Jul 2017jucheng1992/ctmle

Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator.

CAUSAL INFERENCE