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Causal Inference

60 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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Greatest papers with code

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

Local Linear Forests

30 Jul 2018swager/grf

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects.

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

CVPR 2020 KaihuaTang/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

Adapting Text Embeddings for Causal Inference

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

To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.

CAUSAL IDENTIFICATION CAUSAL INFERENCE DIMENSIONALITY REDUCTION LANGUAGE MODELLING TOPIC MODELS WORD EMBEDDINGS