Causal Effect

Causal effect estimation aims to determine the impact of an intervention or treatment on an outcome, accounting for confounding factors that might obscure the true relationship. Current research focuses on improving estimation accuracy and robustness, particularly in complex settings with high-dimensional data, multiple treatments, and unobserved variables, employing techniques like double machine learning, graph neural networks, and Bayesian methods. These advancements are crucial for reliable causal inference across diverse fields, enabling more informed decision-making in areas such as healthcare, social sciences, and business, where understanding cause-and-effect relationships is paramount.

195papers

Papers - Page 12

March 2, 2022

Estimating average causal effects from patient trajectories
Time Varying Average Causal Effect Patient Trajectory Causal Effect

March 1, 2022

On Testability of the Front-Door Model via Verma Constraints
Requirement Testability Instrumental Variable Unmeasured Confounding Front Door Criterion Participation Constraint Unobserved Confounders Causal Effect

February 28, 2022

Estimating causal effects with optimization-based methods: A review and empirical comparison
Narrative Review Causal Inference Causal Effect Empirical Comparison Optimization Method

February 25, 2022

Off-Policy Evaluation with Policy-Dependent Optimization Response
Causal Effect Policy Evaluation Causal Inference Policy Optimization

February 22, 2022

February 7, 2022

January 22, 2022

Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Study
Biased Datasets Societal Bias Biased Model Causal Effect Absolute Stance Bias Empirical Study Racial Bias Persuasive Argument Discriminatory Decision

January 20, 2022

Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making
Causal Effect Sequential Decision Policy Evaluation

January 10, 2022

Style, Content, and the Success of Ideas
Natural Language Processing Human Generated Idea Style Consistency Financial Success Causal Effect Temporal Perspective

December 19, 2021

Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies
Causal Effect Estimation Causal Inference Customer Review Restaurant Review Empirical Evaluation Causal Effect Proxy Dataset

November 24, 2021

Matching Learned Causal Effects of Neural Networks with Domain Priors
Structural Causal Model Causal Effect Domain Prior Neural Network Matching Accuracy Causal Relationship True Causal

November 19, 2021

A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
Treatment Effect Estimation Large Scale Benchmark Individual Treatment Effect Treatment Assignment Causal Inference Task Uplift Modeling Causal Effect

November 16, 2021

Causal policy ranking
Causal Effect Reinforcement Learning Counterfactual Reasoning Causal Learning Algorithm

November 12, 2021

ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects
Observational Data Causal Effect Causal Effect Estimation Alzheimer'S Disease Causal Inference Task Diagnostic Reasoning Benchmark Heterogeneous Effect

November 11, 2021

Variational Auto-Encoder Architectures that Excel at Causal Inference
Generative Approach Causal Inference Observational Data Variational Auto Causal Effect

November 8, 2021

Clustering and Structural Robustness in Causal Diagrams
Transit Network Causal Relation Causal Effect Well Clustered Graph

November 3, 2021

Leveraging Causal Graphs for Blocking in Randomized Experiments
Randomized Experiment Causal Effect Causal Markov Multi Modal Outer Arithmetic Block Causal Graph

Causal Effect

Papers - Page 12

Estimating average causal effects from patient trajectories

On Testability of the Front-Door Model via Verma Constraints

Estimating causal effects with optimization-based methods: A review and empirical comparison

Off-Policy Evaluation with Policy-Dependent Optimization Response

Policy Evaluation for Temporal and/or Spatial Dependent Experiments

Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness

Stochastic Causal Programming for Bounding Treatment Effects

Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records

Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flows

Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Study

Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making

Style, Content, and the Success of Ideas

Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies

Matching Learned Causal Effects of Neural Networks with Domain Priors

A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

Causal policy ranking

ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects

Variational Auto-Encoder Architectures that Excel at Causal Inference

Clustering and Structural Robustness in Causal Diagrams

Leveraging Causal Graphs for Blocking in Randomized Experiments