Conditional Expectation

Conditional expectation, the expected value of a variable given the value of another, is a fundamental concept with broad applications across diverse fields. Current research focuses on developing accurate and efficient data-driven methods for estimating conditional expectations, particularly in high-dimensional settings, employing techniques like neural networks, kernel methods, and tree-based models to address computational challenges and improve estimation accuracy. These advancements are crucial for improving the performance of various applications, including causal inference, reinforcement learning, generative modeling, and missing data imputation, by enabling more robust and reliable predictions and decision-making. The development of theoretically sound and computationally efficient methods for estimating conditional expectations remains a significant area of ongoing research.

Papers

October 28, 2024

Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data
Sophia M Rein, Jing Li, Miguel Hernan, Andrew Beam
Deep Learning Causal Inference Causal Effect Generalized Linear Model Mathematical Formula Classical Data Conditional Expectation Deep Neural Network Estimator

July 18, 2024

Data-Driven Estimation of Conditional Expectations, Application to Optimal Stopping and Reinforcement Learning
George V. Moustakides
Reinforcement Learning Application Proficiency Conditional Density Optimal Action Conditional Expectation

March 6, 2024

Sample size planning for conditional counterfactual mean estimation with a K-armed randomized experiment
Gabriel Ruiz
Randomized Experiment Sample Size Armed Bandit Conditional Expectation Simultaneous Inference Target Inference Conditional Mean

December 9, 2023

Conditional Stochastic Interpolation for Generative Learning
Ding Huang, Jian Huang, Ting Li, Guohao Shen
Conditional Distribution Generative Learning Conditional Sampling Conditional Expectation

October 19, 2023

Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger
High Explainability Predictive Uncertainty Variable Importance Predictive Distribution Entropy Based Permutation Importance Conditional Expectation Uncertainty Aware Model

July 20, 2023

Conditional expectation network for SHAP
Ronald Richman, Mario V. Wüthrich
Generalized Linear Model Shapley Additive Explanation Conditional Network Conditional Expectation Partial Dependence Plot Complex Predictive Model

June 18, 2023

Conditional expectation using compactification operators
Suddhasattwa Das
Manifold Learning Least Square Kernel Hilbert Space Compression Operator Conditional Expectation

February 2, 2023

Conditional expectation with regularization for missing data imputation
Mai Anh Vu, Thu Nguyen, Tu T. Do, Nhan Phan, Nitesh V. Chawla, Pål Halvorsen, Michael A. Riegler, Binh T. Nguyen
Regularization Model Imputation Algorithm Imputation Method Missing Value Data Imputation Imputation Task Conditional Expectation Multicollinearity Issue

December 6, 2022

Extending Universal Approximation Guarantees: A Theoretical Justification for the Continuity of Real-World Learning Tasks
Naveen Durvasula
Universal Approximation Continuous Speech Real World Task Stable Matching Conditional Expectation

November 29, 2022

Causal Inference with Conditional Instruments using Deep Generative Models
Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le
Causal Inference Instrumental Variable Latent Confounders Conditional Expectation Average Causal Effect Treatment Variable

October 12, 2022

A Neural Mean Embedding Approach for Back-door and Front-door Adjustment
Liyuan Xu, Arthur Gretton
Constructive Approach Causal Discovery Neural Topic Treatment Effect Relevant Covariates Conditional Expectation Front Door Adjustment

September 28, 2022

A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations
Jiang Yu Nguwi, Nicolas Privault
Deep Learning Deep Learning Approach Path Dependent Conditional Expectation

July 11, 2022

Shapley Computations Using Surrogate Model-Based Trees
Zhipu Zhou, Jie Chen, Linwei Hu
Decision Tree Shapley Value Surrogate Assisted Version Conditional Expectation SHAP Value Shapley Method Model Tree

December 3, 2021

Computation of conditional expectations with guarantees
Patrick Cheridito, Balint Gersey
Computation Method Formal Guarantee Mean Squared Error Conditional Expectation Numerical Approximation

December 1, 2021

Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling
Avinandan Bose, Pradeep Varakantham
Approximate Dynamic Programming Value Decomposition Conditional Expectation Ride Pooling