Прикладная микроэконометрика с использованием Python: панельные данные, эффекты лечения и причинно-следственное моделирование для экономического анализа: всеобъемлющее руководство

Applied Microeconometrics with Python is a practical guide to using econometric methods for analyzing individual, firm, market, and policy-level data. Designed for economists, analysts, researchers, and technically minded students, this book connects microeconomic theory with applied statistical modeling using Python. The book covers core methods used in modern empirical economics, including panel data analysis, treatment effect estimation, causal modeling, regression design, model interpretation, and applied workflows for economic datasets. Readers will learn how to structure data, estimate models, evaluate assumptions, and interpret results in a clear analytical context. Inside, readers will explore: Panel data methods for repeated observations across individuals, firms, regions, or markets Treatment effect approaches for evaluating policy, business, and economic interventions Causal modeling concepts for separating correlation from plausible economic mechanisms Python-based workflows for cleaning, modeling, testing, and interpreting data Practical applications across labor economics, public policy, market analysis, and financial economics Written with an applied focus, this book avoids unnecessary abstraction while still respecting the rigor required for serious empirical work. It is intended for readers who want to move beyond basic regression and build stronger, more defensible economic analysis using Python.