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Causal Inference

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 )

Papers

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Showing 876–900 of 1722 papers

TitleStatusHype
An evaluation framework for comparing causal inference models—0
Data-Driven Influence Functions for Optimization-Based Causal Inference—0
Virtual Control Group: Measuring Hidden Performance Metrics—0
Causal Inference in Recommender Systems: A Survey and Future DirectionsCode1
Categoroids: Universal Conditional Independence—0
Meta-Causal Feature Learning for Out-of-Distribution Generalization—0
Twin Papers: A Simple Framework of Causal Inference for Citations via CouplingCode0
Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance—0
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization—0
Network inference via process motifs for lagged correlation in linear stochastic processes—0
Inference on Strongly Identified Functionals of Weakly Identified Functions—0
Collaborative causal inference on distributed data—0
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems—0
Valid Inference After Causal Discovery—0
Causal Effect Identification in Uncertain Causal Networks—0
Long-term Causal Effects Estimation via Latent Surrogates Representation LearningCode0
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning ApproachCode0
Forecasting Algorithms for Causal Inference with Panel DataCode0
Causality in cardiorespiratory signals in pediatric cardiac patients—0
The Face of Affective Disorders—0
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Tangential Wasserstein ProjectionsCode0
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment AnalysisCode1
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis—0
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All datasetsIHDPJobsIDHP

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96—Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93—Unverified
3k-NNAverage Treatment Effect Error0.79—Unverified
4CEVAEAverage Treatment Effect Error0.46—Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42—Unverified
6Causal ForestAverage Treatment Effect Error0.4—Unverified
7BCAUS DRAverage Treatment Effect Error0.29—Unverified
8TARNetAverage Treatment Effect Error0.28—Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27—Unverified
10MTDL-KNNAverage Treatment Effect Error0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09—Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08—Unverified
3BARTAverage Treatment Effect on the Treated Error0.08—Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06—Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34—Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31—Unverified
3Average Treatment Effect Error-0.23—Unverified