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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 601–650 of 1722 papers

TitleStatusHype
Using representation balancing to learn conditional-average dose responses from clustered dataCode0
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message LengthCode0
Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach—0
s-ID: Causal Effect Identification in a Sub-PopulationCode0
INTAGS: Interactive Agent-Guided Simulation—0
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments—0
Emoji Promotes Developer Participation and Issue Resolution on GitHub—0
Woolf et als GWAS by subtraction is not useful for cross-generational Mendelian randomization studies—0
Machine Unlearning for Causal Inference—0
Causal Parrots: Large Language Models May Talk Causality But Are Not CausalCode1
Federated Causal Inference from Observational DataCode0
Benchmarking Causal Study to Interpret Large Language Models for Source Code—0
Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?—0
Artificial Intelligence and Aesthetic Judgment—0
Active and Passive Causal Inference Learning—0
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data—0
Uplift Modeling: from Causal Inference to Personalization—0
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations—0
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering—0
Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks—0
Nonlinear Permuted Granger Causality—0
A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009—0
Generalization bound for estimating causal effects from observational network data—0
SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation ModelingCode0
Diffusion Model in Causal Inference with Unmeasured ConfoundersCode0
Causal Effect Estimation on Hierarchical Spatial Graph DataCode0
Scalable Computation of Causal Bounds—0
Improving the Variance of Differentially Private Randomized Experiments through Clustering—0
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference—0
A continuous Structural Intervention Distance to compare Causal Graphs—0
Causal Inference for Banking Finance and Insurance A Survey—0
Learning sources of variability from high-dimensional observational studiesCode0
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network—0
Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference—0
Asymptotically Unbiased Synthetic Control Methods by Density Matching—0
Causality-oriented robustness: exploiting general noise interventionsCode0
An R package for parametric estimation of causal effects—0
Efficient Computation of Counterfactual Bounds—0
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation—0
Unbiased Scene Graph Generation via Two-stage Causal Modeling—0
Root Causal Inference from Single Cell RNA Sequencing with the Negative Binomial—0
Counterfactual Explanation for Fairness in Recommendation—0
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference—0
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal ReasoningCode3
Causal inference for the expected number of recurrent events in the presence of a terminal event—0
Predictive Coding beyond Correlations—0
Towards Trustworthy Explanation: On Causal RationalizationCode0
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions—0
Estimating the Causal Effect of Early ArXiving on Paper AcceptanceCode1
Learning Conditional Instrumental Variable Representation for Causal Effect EstimationCode0
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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