[(Counterfactuals and Causal Inference: Methods and Principles for Social Research)] [Author: Stephen L. Morgan] published on

Analytical Methods for Social Research

by Stephen L. Morgan

Did mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual s labor market earnings? Did the use of a butterfly ballot in some Florida counties in the 2000 presidential election cost Al Gore votes? Simple cause-and-effect questions such as these are the motivation for much empirical work in the social sciences. In this book, the counterfactual model of causality for observational data analysis is presented, and methods for causal effect estimation are demonstrated using examples from sociology, political science, and economics."

Genres: Sociology, Economics, Academic, Reference, Social Science, Philosophy, Mathematics

Unknown Binding

First published January 1, 2007

Reviews

[Holcombea · Rating 3 out of 5]

Caveat: I haven't gotten very far into this book - maybe only read 20% of it. It's been a bit bland, sort of an even-handed textbook approach perhaps without as clear a unifying framework as I was hoping for. Some of the writing of Judea Pearl is exciting; this isn't. I was also hoping for some descriptions of how well all these techniques have been turned out to get the causality right in the very few cases where experiments could ultimately be done. Young & Karr (2011) show what a failure the multiple linear regression approach has been for revealing causality in the case of nutrition. I have yet to see anything that grapples with that, but I'm completely outside the field, so there probably is plenty of stuff out there that I haven't come across.

[Görkem Saylam · Rating 5 out of 5]

Counterfactuals and Causal Inference | Stephen L. Morgan
Scoring Rubric
1: baseline
2: creative contextualization bcs of covering new analytical methods for social research
2: creative conceptualization bcs of new technical representation on causal inference
5: total points by 5

[Connor Oswald · Rating 4 out of 5]

took me long enough... Good stuff on IV, Weighted regression, matching and theory

[Seth Hunter · Rating 3 out of 5]

Strengths: multiple representations, PSM, IV

Words, graphs, tables, and equations are used to convey the potential outcomes framework as applied to matching and regression estimators - really liked here chapters.

Discussion of panel methods was rushed and some tabular examples weren't transparent enough.

Great as a digital edition because you can bounce between equations, text, footnotes, etc.

[Dennis · Rating 3 out of 5]

http://pro-libertate.net/20151123/281...

[Maikel · Rating 2 out of 5]

Decent introduction into causal analysis; treatment of time series and panel data methods could be expanded.