Causal inference

Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed.

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Causal Inference 490 51.42%
Causal Discovery 38 3.99%
Decision Making 34 3.57%
BIG-bench Machine Learning 21 2.20%
Fairness 20 2.10%
Time Series Analysis 19 1.99%
Selection bias 16 1.68%
Recommendation Systems 14 1.47%
Reinforcement Learning (RL) 10 1.05%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories