I'm Mojtaba, an economist, statistician, and data scientist with a background in microeconometrics, causal inference, game theory, experimentation, and machine learning.
My work sits at the intersection of economics, statistics, and artificial intelligence, with a particular interest in rigorous methods for decision-making under uncertainty. I am especially interested in combining Causal Machine Learning, Reinforcement Learning (RL), Dynamic Programming (DP), empirical methods, and modern statistical techniques to study complex decision problems.
My research and technical interests include:
- Causal Inference & Causal Machine Learning — treatment-effect estimation, heterogeneous treatment effects, double/debiased machine learning, uplift modeling, instrumental variables, difference-in-differences, and synthetic control.
- Experimentation & Decision Science — A/B testing, experimental design, sequential experimentation, policy evaluation, and the connection between experimentation and optimal decision-making.
- Reinforcement Learning & Dynamic Programming — sequential decision-making, Markov decision processes, contextual bandits, policy learning, and dynamic optimization.
- Empirical & Statistical Methods — econometrics, Bayesian and frequentist inference, predictive modeling, time-series analysis, uncertainty quantification, and statistical learning.
- Machine Learning & AI — supervised and unsupervised learning, recommendation systems, representation learning, and applications of modern AI methods to economic and business problems.
- Networks & Online Platforms — complex networks, marketplace dynamics, recommendation systems, strategic interactions, and the economics of digital platforms.
I have worked across both academic research and industry, including the School of Public Policy and Canadian Tire Corporation, applying statistical, econometric, machine-learning, and experimentation methods to real-world problems.
I am particularly interested in research that connects prediction, causality, experimentation, and optimization — moving beyond predicting what will happen toward understanding what actions should be taken, for whom, and under what conditions.
- 👯 I'm always interested in collaborating on research, open-source projects, and interesting methodological problems.
- ♟️ Outside of research, I enjoy chess, card and strategy games, swimming, and cooking.
Find out more about my work and feel free to connect with me here:
