-PDF Download- Impact Evaluation Treatment Effects And Causal Analysis Basic Definitions Assumptions And Randomised Experiments 2 An Introduction To Nonparametric Identification And Estimation 3 Selection On Observables Matching Regression And Propensity Score Estimators 4 Selection On Unobservables Nonparametric Iv And Structural Equation Approaches 5 Difference In Differences Estimation Selection On Observables And Unobservables 6 Regression Discontinuity Design 7 Distributional Policy Analysis And Quantile Treatment Effects 8 Dynamic Treatment Evaluation EBOOK

Impact Evaluation, Treatment Effects and Causal Analysis: Basic Definitions, Assumptions, and Randomised Experiments; 2. An Introduction to Nonparametric Identification and Estimation; 3. Selection on Observables: Matching, Regression and Propensity Score Estimators; 4. Selection on Unobservables: Nonparametric IV and Structural Equation Approaches; 5. Difference-in-Differences Estimation: Selection on Observables and Unobservables; 6. Regression Discontinuity Design; 7. Distributional Policy Analysis and Quantile Treatment Effects; 8. Dynamic Treatment Evaluation

Author: Markus Fröhlich

Publisher:

Published: 2019

Total Pages:

ISBN-13: 9781107337008

"This book on advanced econometrics is intended to familiarise the reader with technical developments in the area of econometric which is known under the label treatment e ect estimation, or impact or policy evaluation. In this book we have tried to combine the intuitive reasoning for identi cation and estimation with the econometric and statistical rigorousness. This holds especially for the complete list of stochastic assumptions and their implications in practise. Moreover, for both, identi cation and estimation we focus mostly on nonparametric methods (i.e. our methods are not based on speci c pre-speci ed models or functional forms) in order to provide methods that are quite generally valid. Graphs and a number examples of evaluation studies are applied to explain how sources of exogenous variation can be explored for disentangling causality from correlation"--

Impact Evaluation

Author: Markus Frölich

Publisher: Cambridge University Press

Published: 2019-03-21

Total Pages: 431

ISBN-13: 1107042461

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Encompasses the main concepts and approaches of quantitative impact evaluations, used to consider the effectiveness of programmes, policies, projects or interventions. This textbook for economics graduate courses can also serve as a manual for professionals in research institutes, governments, and international organizations.

Mostly Harmless Econometrics

Author: Joshua D. Angrist

Publisher: Princeton University Press

Published: 2009-01-04

Total Pages: 392

ISBN-13: 0691120358

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In addition to econometric essentials, this book covers important new extensions as well as how to get standard errors right. The authors explain why fancier econometric techniques are typically unnecessary and even dangerous.

The Estimation of Causal Effects by Difference-in-difference Methods

Author: Michael Lechner

Publisher: Foundations and Trends(r) in E

Published: 2011

Total Pages: 72

ISBN-13: 9781601984982

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This monograph presents a brief overview of the literature on the difference-in-difference estimation strategy and discusses major issues mainly using a treatment effect perspective that allows more general considerations than the classical regression formulation that still dominates the applied work.

Handbook on Impact Evaluation

Author: Shahidur R. Khandker

Publisher: World Bank Publications

Published: 2009-10-13

Total Pages: 262

ISBN-13: 082138029X

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Public programs are designed to reach certain goals and beneficiaries. Methods to understand whether such programs actually work, as well as the level and nature of impacts on intended beneficiaries, are main themes of this book.

Causal Inference in Statistics

Author: Judea Pearl

Publisher: John Wiley & Sons

Published: 2016-01-25

Total Pages: 162

ISBN-13: 1119186862

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CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

A Review of Recent Developments in Impact Evaluation

Author: Asian Development Bank

Publisher: Asian Development Bank

Published: 2011-02-01

Total Pages: 202

ISBN-13: 9290922923

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Impact evaluation aims to answer whether and to what extent a development intervention has delivered its intended effects, thus enabling evidence-based policy making. The desire for more hard evidence of the effectiveness of development interventions has fueled a growing interest in rigorous impact evaluation in the international development community. This report discusses the fundamental challenge of impact evaluation, which is to credibly attribute the impact, if any, to the intervention concerned. It then discusses the merits and limitations of various impact evaluation methods. It also presents a survey of recent applications of impact evaluation, focusing on the typical evaluation problems looked at, methods used, and key findings. The report includes six case studies and outlines practical steps in implementing an impact evaluation.

Impact Evaluation of Development Interventions

Author: Howard White

Publisher: Asian Development Bank

Published: 2017-12-01

Total Pages: 177

ISBN-13: 9292610597

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Impact evaluation is an empirical approach to estimating the causal effects of interventions, in terms of both magnitude and statistical significance. Expanded use of impact evaluation techniques is critical to rigorously derive knowledge from development operations and for development investments and policies to become more evidence-based and effective. To help backstop more use of impact evaluation approaches, this book introduces core concepts, methods, and considerations for planning, designing, managing, and implementing impact evaluation, supplemented by examples. The topics covered range from impact evaluation purposes to basic principles, specific methodologies, and guidance on field implementation. It has materials for a range of audiences, from those who are interested in understanding evidence on "what works" in development, to those who will contribute to expanding the evidence base as applied researchers.

An Introduction to Causal Inference

Author: Judea Pearl

Publisher: Createspace Independent Publishing Platform

Published: 2015

Total Pages: 0

ISBN-13: 9781507894293

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This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.

Microeconometrics

Author: A. Colin Cameron

Publisher: Cambridge University Press

Published: 2005-05-09

Total Pages: 1058

ISBN-13: 1139444867

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This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.