Causal Inference
425 papers with code • 3 benchmarks • 8 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 )
Libraries
Use these libraries to find Causal Inference models and implementations
IHDP
NTPairs
ICLR Database
Most implemented papers
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.
Interpretable Almost Matching Exactly for Causal Inference
Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.
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.
Using Embeddings to Correct for Unobserved Confounding in Networks
vveitch/causal-network-embeddings
•
•
NeurIPS 2019
We validate the method with experiments on a semi-synthetic social network dataset.
Estimating Transfer Entropy via Copula Entropy
Causal discovery is a fundamental problem in statistics and has wide applications in different fields.
Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions
We study causal inference under case-control and case-population sampling.
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
SUTDCV/SUTD-TrafficQA
•
•
CVPR 2021
In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10, 080 in-the-wild videos and annotated 62, 535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios.
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
