Advances in Causal Inference and Decision-Making under Uncertainty

The field of causal inference and decision-making under uncertainty is rapidly advancing, with a focus on developing novel methods for learning from data and making informed decisions in complex environments. Recent work has explored the intersection of causal inference, reinforcement learning, and graph neural networks, leading to new approaches for causal discovery, decision-making, and representation learning. Notably, researchers have proposed innovative solutions for addressing challenges such as confounding variables, selection bias, and distributional shifts. These advances have significant implications for applications in marketing optimization, recommendation systems, and online advertising.

Some noteworthy papers in this area include: The paper on Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems, which introduces a novel framework for causal inference in strategic environments. The paper on Competition is the key: A Game Theoretic Causal Discovery Approach, which presents a game-theoretic reinforcement learning framework for causal discovery with finite-sample guarantees. The paper on Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization, which proposes a bi-level optimization framework for bridging observational and experimental data in marketing optimization.

Sources

How to Sell High-Dimensional Data Optimally

Large-scale User Game Lifecycle Representation Learning

Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems

Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics

Graph Learning is Suboptimal in Causal Bandits

Exploration via Feature Perturbation in Contextual Bandits

Learning Peer Influence Probabilities with Linear Contextual Bandits

Scalable LinUCB: Low-Rank Design Matrix Updates for Recommenders with Large Action Spaces

Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization: Bridging Observational and Experimental Data

Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards

Competition is the key: A Game Theoretic Causal Discovery Approach

Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization

Balanced Popularity in Multi-Product Billboard Advertisement

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