Razieh Nabi

Drawing valid causal conclusions from data is impeded by various factors such as the presence of unmeasured confounders, curse of dimensionality, missing and censored values, measurement error, social contagion, network interference, and data that reflect historical patterns of discrimination and inequality. The focus of my research is the development of novel causal methodologies to address these pressing challenges. My research draws on methodological insights from both machine learning/artificial intelligence, especially using graphical models, and statistical theory, especially semiparametric statistics. My applications of interest include healthcare, social justice, and public policy.