Combining Observational and Experimental Data to Improve Large-Scale Decision-Making. - Carlos Fernández-Loría; Foster J. Provost (ICIS, 2020)

Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. - Carlos Fernández-Loría; Foster J. Provost (CoRR, 2021)

Learning the Ranking of Causal Effects with Confounded Data. - Carlos Fernández-Loría; Jorge Loría (CoRR, 2022)

Causal Classification: Treatment Effect Estimation vs. Outcome Prediction. - Carlos Fernández-Loría; Foster J. Provost (J. Mach. Learn. Res., 2022)

Evolution of Referrals over Customers' Life Cycle: Evidence from a Ride-Sharing Platform. - Carlos Fernández-Loría; Maxime C. Cohen; Anindya Ghose (Inf. Syst. Res., 2023)

A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation. - Carlos Fernández-Loría; Foster J. Provost; Jesse Anderton; Benjamin A. Carterette; Praveen Chandar (Inf. Syst. Res., 2023)

Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models. - Carlos Fernández-Loría; Yanfang Hou; Foster J. Provost; Jennifer Hill (CoRR, 2024)

Causal Inference Isn't Special: Why It's Just Another Prediction Problem. - Carlos Fernández-Loría (CoRR, 2025)

Honesty in Causal Forests: When It Helps and When It Hurts. - Yanfang Hou; Carlos Fernández-Loría (CoRR, 2025)