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 )
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
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ParaBank
NTPairs
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
uber/causalml
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CausalML is a Python implementation of algorithms related to causal inference and machine learning.
Orthogonal Random Forest for Causal Inference
We provide a consistency rate and establish asymptotic normality for our estimator.
Double/Debiased Machine Learning for Treatment and Causal Parameters
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
microsoft/DiCE
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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.
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.
Structural Intervention Distance (SID) for Evaluating Causal Graphs
FenTechSolutions/CausalDiscoveryToolbox
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To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
Unbiased Scene Graph Generation from Biased Training
KaihuaTang/Scene-Graph-Benchmark.pytorch
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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".
Ranked #1 on
Scene Graph Generation
on Visual Genome
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
KaihuaTang/Long-Tailed-Recognition.pytorch
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NeurIPS 2020
On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.
Ranked #12 on
Long-tail Learning
on ImageNet-LT
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.
