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Causal Inference

30 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

Structural Intervention Distance (SID) for Evaluating Causal Graphs

5 Jun 2013Diviyan-Kalainathan/CausalDiscoveryToolbox

To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).

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

Using Text Embeddings for Causal Inference

29 May 2019blei-lab/causal-text-embeddings

A key insight is that causal adjustment requires only the aspects of text that are predictive of both the treatment and outcome.

CAUSAL INFERENCE

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

12 Jun 2019orcax/PGPR

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.

CAUSAL INFERENCE DECISION MAKING KNOWLEDGE GRAPHS

Adapting Neural Networks for the Estimation of Treatment Effects

5 Jun 2019claudiashi57/dragonnet

We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.

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