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Causal Inference and Machine Learning In Economics, Social, and Health Sciences.pdf
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Causal Inference and Machine Learning in Economics, Social, and Health Sciences bridges the gap between
modern machine learning methods and the applied needs of economists, public health researchers, and
social scientists. Designed with students and practitioners in mind, it introduces machine learning
through the lens of causal inference, offering a rigorous yet accessible roadmap for using data to answer
real-world policy questions.
It combines econometric and machine learning methods such as penalized regressions, random
forests, boosting, double machine learning, and the most up-to-date estimation methods for addressing
selection on observables (e.g., matching, AIPW) and unobservables (e.g., instrumental variables, difference-
in-differences, synthetic control). Readers learn how to estimate treatment effects, uncover heterogeneity,
and work with high-dimensional data, while gaining clarity on assumptions, trade-offs, and limitations.
The resource also covers advanced and often underrepresented topics such as time series forecasting with
machine learning methods, neural networks and deep learning, and core optimization algorithms like
gradient descent. Each method is introduced with intuition, formal treatment, and applied examples from
economics, health, labor, and development studies. It places special emphasis on transparency,
identification, and interpretability.
Beyond introducing models, it provides step-by-step guidance from raw data to estimation, showing
not just what works, but how and why—both methodologically and computationally. Unlike many texts
that rely on pre-built software or assume deep technical knowledge, it builds from foundational
concepts such as estimation, error decomposition, and bias–variance trade-offs, then progresses to
advanced machine learning approaches. Simulation-based pedagogy helps readers visualize model
behavior under known conditions, enabling researchers and students alike to see how statistical tools
perform across diverse empirical settings.
A distinctive feature of the it is its focus on when and how to use predictive versus causal models. Rather
than treating them as separate tasks, it shows how each can inform the other. Practical insights, diagnostics,
and examples guide readers in selecting appropriate tools based on research goals and data characteristics.
With its clear style, practical code in R, and integrated approach to prediction and causality, it
is an essential resource for applied researchers, students, and anyone using data to inform policy and
decision-making.
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