4 papers
Honesty in Causal Forests: When It Helps and When It Hurts
Yanfang Hou, Carlos Fernández-LorÃa
Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard practic…
Causal Post-Processing of Predictive Models
Carlos Fernández-LorÃa, 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-LorÃa, Jorge LorÃa
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…
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
Carlos Fernández-LorÃa
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…