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In recent years, interest in rigorous impact evaluation has grown
tremendously in policy-making, economics, public health, social
sciences and international relations. Evidence-based policy-making
has become a recurring theme in public policy, alongside greater
demands for accountability in public policies and public spending,
and requests for independent and rigorous impact evaluations for
policy evidence. Frölich and Sperlich offer a comprehensive and
up-to-date approach to quantitative impact evaluation analysis,
also known as causal inference or treatment effect analysis,
illustrating the main approaches for identification and estimation:
experimental studies, randomization inference and randomized
control trials (RCTs), matching and propensity score matching and
weighting, instrumental variable estimation,
difference-in-differences, regression discontinuity designs,
quantile treatment effects, and evaluation of dynamic treatments.
The book is designed for economics graduate courses but can also
serve as a manual for professionals in research institutes,
governments, and international organizations, evaluating the impact
of a wide range of public policies in health, environment,
transport and economic development.
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