Open AccessBook
Counterfactuals and Causal Inference: Methods and Principles for Social Research
TLDR
In this article, the authors proposed a method to estimate causal effects by conditioning on observed variables to block backdoor paths in observational social science research, but the method is limited to the case of causal exposure and identification criteria for conditioning estimators.Abstract:
Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model 3. Causal graphs Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators 5. Matching estimators of causal effects 6. Regression estimators of causal effects 7. Weighted regression estimators of causal effects Part IV. Estimating Causal Effects When Backdoor Conditioning Is Ineffective: 8. Self-selection, heterogeneity, and causal graphs 9. Instrumental-variable estimators of causal effects 10. Mechanisms and causal explanation 11. Repeated observations and the estimation of causal effects Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science.read more
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An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
Journal ArticleDOI
Recent developments in the econometrics of program evaluation
TL;DR: In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects as discussed by the authors, which has reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization, and other areas in empirical microeconomics.
Posted Content
Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects
TL;DR: In this paper, the authors investigated conditions sufficient for identification of average treatment effects using instrumental variables and showed that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect.
Journal ArticleDOI
Causal Inference without Balance Checking: Coarsened Exact Matching
TL;DR: It is shown that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use.
References
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Book
Hierarchical Linear Models: Applications and Data Analysis Methods
TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Book
Experimental and Quasi-Experimental Designs for Generalized Causal Inference
TL;DR: In this article, the authors present experiments and generalized Causal inference methods for single and multiple studies, using both control groups and pretest observations on the outcome of the experiment, and a critical assessment of their assumptions.
Book
Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education
TL;DR: In this paper, the effects of investment in education and training on earnings and employment are discussed. But the authors focus on the relationship between age and earnings and do not explore the relation between education and fertility.