About
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 )
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Subtasks
Datasets
Greatest papers with code
DoWhy: An End-to-End Library for Causal Inference
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.
CausalML: Python Package for Causal Machine Learning
CausalML is a Python implementation of algorithms related to causal inference and machine learning.
Orthogonal Random Forest for Causal Inference
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.
Double/Debiased Machine Learning for Treatment and Causal Parameters
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
Local Linear Forests
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.
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
• interpretml/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.
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".
Ranked #1 on
Scene Graph Generation
on Visual Genome
Structural Intervention Distance (SID) for Evaluating Causal Graphs
• FenTechSolutions/CausalDiscoveryToolbox
•
To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
NeurIPS 2020
• KaihuaTang/Long-Tailed-Recognition.pytorch
•
On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.
CAUSAL INFERENCE IMAGE CLASSIFICATION INSTANCE SEGMENTATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION
Masked Gradient-Based Causal Structure Learning
• xunzheng/notears
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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.