3 accepted papers
Estimating heterogeneous treatment effects is central to data-driven decision-making, yet industrial applications often face a fundamental tension between limited randomized controlled trial (RCT) budgets and abundant but biased observational data collected under historical targeting policies. Altho…
Influence Maximization (IM) seeks a seed set to maximize information dissemination in a network. Elegant IM algorithms could naturally extend to cases where each node is equipped with a specific weight, reflecting individual gains to measure its importance. In prevailing literature, these gains are…
Causal effect estimation is a crucial theoretical tool in uncertainty analysis. The challenge of unobservable confoundings has raised concerns regarding quantitative causality computation. To address this issue, proxy control has become popular, employing auxiliary variables W as proxies for the con…