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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

DoWhy: An End-to-End Library for Causal Inference

9 Nov 2020microsoft/dowhy

In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent.

CAUSAL INFERENCE

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

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

10 Nov 2020interpretml/DiCE

These feature attributions convey how important a feature is to changing the classification outcome of a model, especially on whether a subset of features is necessary and/or sufficient for that change, which feature attribution methods are unable to provide.

CAUSAL INFERENCE FEATURE IMPORTANCE

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

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

Masked Gradient-Based Causal Structure Learning

18 Oct 2019xunzheng/notears

We next utilize the augmented form to develop a masked structure learning method that can be efficiently trained using gradient-based optimization methods, by leveraging a smooth characterization on acyclicity and the Gumbel-Softmax approach to approximate the binary adjacency matrix.

CAUSAL DISCOVERY CAUSAL INFERENCE