1 citations · 1 across the 3 of their papers we have counts for
5 papers
Honesty in Causal Forests: When It Helps and When It Hurts
Yanfang Hou, Carlos Fernández-Loría
Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard practic…
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
Carlos Fernández-Loría
Causal inference is often portrayed as fundamentally distinct from predictive modeling, with its own terminology, goals, and intellectual challenges. But at its core, causal infere…
Causal Post-Processing of Predictive Models
Carlos Fernández-Loría, Yanfang Hou, Foster Provost +1
Organizations increasingly rely on predictive models to decide who should be targeted for interventions, such as marketing campaigns, customer retention offers, or medical treatmen…
Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
Carlos Fernández-Loría, Jorge Loría
Who should we prioritize for treatment when causal effects cannot be estimated? In practice, organizations often rely on predictive proxies: ads are targeted using purchase probabi…
A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation
Carlos Fernández-Loría, Foster Provost, Jesse Anderton +2
This study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and has received significant attentio…