Flexible and Scalable Deep Learning with MMLSpark. - Mark Hamilton; Sudarshan Raghunathan; Akshaya Annavajhala; Danil Kirsanov; Eduardo de Leon; Eli Barzilay; Ilya Matiach; Joe Davison; Maureen Busch; Miruna Oprescu; Ratan Sur; Roope Astala; et al. (PAPIs, 2017)

Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation. - Miruna Oprescu; Vasilis Syrgkanis; Zhiwei Steven Wu (CoRR, 2018)

Orthogonal Random Forest for Causal Inference. - Miruna Oprescu; Vasilis Syrgkanis; Zhiwei Steven Wu (ICML, 2019)

Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. - Vasilis Syrgkanis; Victor Lei; Miruna Oprescu; Maggie Hei; Keith Battocchi; Greg Lewis (NeurIPS, 2019)

Online Learning with Optimism and Delay. - Genevieve Flaspohler; Francesco Orabona; Judah Cohen; Soukayna Mouatadid; Miruna Oprescu; Paulo Orenstein; Lester Mackey (ICML, 2021)

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber. - Vasilis Syrgkanis; Greg Lewis; Miruna Oprescu; Maggie Hei; Keith Battocchi; Eleanor Dillon; Jing Pan; Yifeng Wu; Paul Lo; Huigang Chen; Totte Harinen; Jeong-Yoon Lee (KDD, 2021)

Estimating the Long-Term Effects of Novel Treatments. - Keith Battocchi; Eleanor Dillon; Maggie Hei; Greg Lewis; Miruna Oprescu; Vasilis Syrgkanis (NeurIPS, 2021)

Learned Benchmarks for Subseasonal Forecasting. - Soukayna Mouatadid; Paulo Orenstein; Genevieve Flaspohler; Miruna Oprescu; Judah Cohen; Franklyn Wang; Sean Knight; Maria Geogdzhayeva; Sam Levang; Ernest Fraenkel; Lester Mackey (CoRR, 2021)

Adaptive Bias Correction for Improved Subseasonal Forecasting. - Soukayna Mouatadid; Paulo Orenstein; Genevieve Flaspohler; Judah Cohen; Miruna Oprescu; Ernest Fraenkel; Lester Mackey (CoRR, 2022)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects. - Nathan Kallus; Miruna Oprescu (AISTATS, 2023)

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. - Miruna Oprescu; Jacob Dorn; Marah Ghoummaid; Andrew Jesson; Nathan Kallus; Uri Shalit (ICML, 2023)

SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking. - Soukayna Mouatadid; Paulo Orenstein; Genevieve Flaspohler; Miruna Oprescu; Judah Cohen; Franklyn Wang; Sean Knight; Maria Geogdzhayeva; Sam Levang; Ernest Fraenkel; Lester Mackey (NeurIPS, 2023)

Low-rank MDPs with Continuous Action Spaces. - Miruna Oprescu; Andrew Bennett; Nathan Kallus (AISTATS, 2024)

Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. - Andrew Bennett; Nathan Kallus; Miruna Oprescu; Wen Sun 002; Kaiwen Wang (NeurIPS, 2024)

Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data. - Miruna Oprescu; Nathan Kallus (NeurIPS, 2024)

GST-UNet: Spatiotemporal Causal Inference with Time-Varying Confounders. - Miruna Oprescu; David Keetae Park; Xihaier Luo; Shinjae Yoo; Nathan Kallus (CoRR, 2025)

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields. - David Keetae Park; Xihaier Luo; Guang Zhao; Seungjun Lee; Miruna Oprescu; Shinjae Yoo (CoRR, 2025)

Efficient Adaptive Experimentation with Non-Compliance. - Miruna Oprescu; Brian M. Cho; Nathan Kallus (CoRR, 2025)