Causal Inference

146 papers with code • 1 benchmarks • 4 datasets

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 )

Greatest papers with code

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

microsoft/dowhy 9 Nov 2020

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

uber/causalml 25 Feb 2020

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

Causal Inference

Orthogonal Random Forest for Causal Inference

Microsoft/EconML 9 Jun 2018

We provide a consistency rate and establish asymptotic normality for our estimator.

Causal Inference

Double/Debiased Machine Learning for Treatment and Causal Parameters

Microsoft/EconML 30 Jul 2016

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

Causal Inference

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

microsoft/DiCE 10 Nov 2020

In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction.

Causal Inference Counterfactual Explanation +1

Local Linear Forests

swager/grf 30 Jul 2018

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

FenTechSolutions/CausalDiscoveryToolbox 5 Jun 2013

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

KaihuaTang/Scene-Graph-Benchmark.pytorch CVPR 2020

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 +1

Masked Gradient-Based Causal Structure Learning

xunzheng/notears 18 Oct 2019

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