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Causal Inference

50 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.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

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CausalML: Python Package for Causal Machine Learning

25 Feb 2020uber/causalml

CausalML is a Python implementation of algorithms related to causal inference and machine learning.

CAUSAL INFERENCE

Orthogonal Random Forest for Causal Inference

9 Jun 2018Microsoft/EconML

We show that under mild assumptions 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 2013FenTechSolutions/CausalDiscoveryToolbox

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

CAUSAL INFERENCE

Unbiased Scene Graph Generation from Biased Training

27 Feb 2020KaihuaTang/Scene-Graph-Benchmark.pytorch

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".

CAUSAL INFERENCE GRAPH GENERATION SCENE GRAPH GENERATION

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

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

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

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