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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 1451–1500 of 1722 papers

TitleStatusHype
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach—0
Causal inference using deep neural networks—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Causal Inference Using LLM-Guided Discovery—0
Causal inference using the algorithmic Markov condition—0
Causal Inference Using Tractable Circuits—0
Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables—0
Causal Inference via Conditional Kolmogorov Complexity using MDL Binning—0
Causal Inference via Kernel Deviance Measures—0
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications—0
Predictive Coding beyond Correlations—0
Causal inference with Bayes rule—0
Causal Inference with Complex Treatments: A Survey—0
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder—0
Causal Inference with Conditional Instruments using Deep Generative Models—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Causal Inference with Deep Causal Graphs—0
Improving the Variance of Differentially Private Randomized Experiments through Clustering—0
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants—0
Causal inference with imperfect instrumental variables—0
Causal Inference with Large Language Model: A Survey—0
Causal Inference with Latent Variables: Recent Advances and Future Prospectives—0
Causal inference with Machine Learning-Based Covariate Representation—0
Causal Inference With Selectively Deconfounded Data—0
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach—0
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization—0
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network—0
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift—0
Causal Interpretations in Observational Studies: The Role of Sociocultural Backgrounds and Team Dynamics—0
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition—0
Causal Intervention for Weakly-Supervised Semantic Segmentation—0
Causal Interventions in Bond Multi-Dealer-to-Client Platforms—0
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text—0
Causality-Aided Trade-off Analysis for Machine Learning Fairness—0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Causality and Explainability for Trustworthy Integrated Pest Management—0
Causality-Aware Neighborhood Methods for Recommender Systems—0
Causality, Causal Discovery, and Causal Inference in Structural Engineering—0
Causality-Driven Audits of Model Robustness—0
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods—0
Causality for Natural Language Processing—0
Causality in cardiorespiratory signals in pediatric cardiac patients—0
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness—0
Causality in the Can: Diet Coke's Impact on Fatness—0
Causal knowledge graph analysis identifies adverse drug effects—0
Transfer Learning for Individual Treatment Effect Estimation—0
Causal Links Between Anthropogenic Emissions and Air Pollution Dynamics in Delhi—0
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction—0
Causally Consistent Normalizing Flow—0
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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