Resources

PDF Links for Jeff’s Off-Hand References

What Jeff calls it	Citation (hover and/or click for PDF link)
“The Multilevel book”	Gelman and Hill (2007)
“The Koller book”	Koller and Friedman (2009)
“The non-technical Pearl book”	Pearl and Mackenzie (2018)
“The technical Pearl book” (containing, e.g., axioms and proofs)	Pearl (2000)
“The French Revolution paper”	Barron et al. (2018)

Core Textbooks

As mentioned on the syllabus, the field of causal inference in general (and causal inference for computational social science in particular) moves excitingly-fast, such that the material has yet to “congeal” into a single, all-encompassing textbook. Nonetheless, the following three books cover a substantial amount of ground (described in more detail below each citation), so that together they form a coherent “three-volume textbook” for this class! If you can only read three books this summer, read these :)

Morgan and Winship (2015)

Morgan and Winship, Counterfactuals and Causal Inference: Methods and Principles for Social Research [PDF]

This is the book which comes closest to being an all-encompassing, single textbook for the class! It brings together different “strands” of causal modeling research (since each field—economics, bioinformatics, sociology, etc.—tends to use its own notation and vocabulary), unifying them into a single approach. The only reason we can’t use it as the main textbook is because it hasn’t been updated since 2015, and most of the assignments in this class use computational tools from 2018 onwards!

Angrist and Pischke (2014)

Angrist and Pischke, Mastering ’Metrics: The Path from Cause to Effect (Angrist and Pischke 2014) [PDF]

This book is included as the second of the three “core” texts mainly because, it uses the language of causality specific to Econometrics, the language that is most familiar to me from my PhD training in Political Economy. However, if you tend to learn better by example, it also does a good job of foregrounding specific examples (like evaluating charter schools and community policing policies), so that the methods emerging naturally from attempts to solve these puzzles when association methods like linear regression fail to capture their causal linkages.

Pearl and Mackenzie (2018)

Pearl and Mackenzie, The Book of Why: The New Science of Cause and Effect [EPUB]

This book contrasts with the Angrist and Pischke book in using the language of causality formed within Computer Science rather than Economics. It can be a good starting point especially if you’re unfamiliar with the heavy use of diagrams for scientific modeling—basically, whereas Angrist and Pischke’s first instinct is to use (sometimes informal) equations like $y=mx+b$ to explain steps in the procedures, Pearl and Mackenzie’s instinct would be to instead use something like $\overline{)x}\stackrel{m,b}{⟶}\overline{)y}$ to represent the same concept (in this case, a line with slope $m$ and intercept $b$!).

Topic-Specific Textbooks

Probabilistic Graphical Models

Koller and Friedman (2009), Probabilistic Graphical Models: Principles and Techniques

Chapter 2, with an overview of probability theory and graph theory, is included in the Syllabus as the recommended reading for Week 2! Then, Chapter 3 will give you the core concepts of Bayes Nets specifically, while Chapter 6 will add in a few of the useful additional tools we use, like plate notation.

Multilevel Modeling

Gelman and Hill (2007), Data Analysis Using Regression and Multilevel/Hierarchical Models

Reference Texts

In contrast to the books in the “Core Textbooks” section, these books are not “the” textbooks for the class! These are here instead as reference books, to keep on hand (a) for when the lectures or the above books are unclear on some topic, and/or (b) for deeper dives into certain topics (where the latter may become a more relevant mission for you as we move towards the final project 😉)

Pearl (2000), Causality: Models, Reasoning, and Inference

As mentioned during Week 2, this is the book containing the fully-developed “unified theory” of causality, starting from a set of axioms and deriving the possibilities of causal inference as formal theorems.

Within “pure” mathematics, if you kept digging into the foundations of things like calculus or algebra, you would eventually arrive at Alfred North Whitehead and Bertrand Russell’s Principia Mathematica—Pearl (2000) is that but for causality: the foundational axioms and core theorems are all in here.

Hume (1748), An Enquiry Concerning Human Understanding

This book serves as the philosophical “jumping off point” for causality: you can think of it like, there’s a nice progress-narrative of the human study of causality, that starts with the uncomfortable questions raised in Hume (1748) about the possibility (or impossibility!) of inferring knowledge of causality via inductive reasoning, and culminates in Pearl (2000).

Sperber (1996), Explaining Culture: A Naturalistic Approach

Mentioned in a footnote in Week 1, this is a surprisingly-old book that made waves in certain communities (like, e.g., among people like me who geek about studying culture quantitatively), by essentially proposing a “research program” for the rigorous quantitative/empirical study of culture. In Jeff’s perfect world, this book would spark a progress-narrative in the same way that Hume (1748)

Applied Examples

The Holy Grail (But, Field = Comparative Politics)

Kalyvas (2006), The Logic of Violence in Civil War

This book is essentially… like, when you read stories about people spending decades hand-carving pathways through a mountain using only a hammer and chisel, this is the social science equivalent of that. A painstaking labor-of-love book that checks every single box I can think of in terms of causally-focused computational social science. It’s my model for any research I try to carry out.

History

Barron et al. (2018), “Individuals, Institutions, and Innovation in the Debates of the French Revolution”

Blaydes, Grimmer, and McQueen (2018), “Mirrors for Princes and Sultans: Advice on the Art of Governance in the Medieval Christian and Islamic Worlds”

Econometric Policy Evaluation

Björkegren, Blumenstock, and Knight (2025), “What Do Policies Value?”, Review of Economic Studies [PDF]

Another “holy grail” paper, but in this case more for the Data Ethics and Policy course than this course. However, it does touch substantially on the issue of associational vs. causal inference, especially in terms of how going towards causality is a “high stakes” endeavor here, since we’re inferring normative ethical values from data (as opposed to descriptive statistics like the magnitude of the causal effect $X\to Y$)

Video Resources

To me (as in, given how my brain works), there are certain topics which I’ve spent hours trying to understand via reading, only to realize that there’s some simple diagram or animation out there which “clicks” it in my mind 10000 times more effectively than the reading ever would.

So, to that end, these video resources are just as important as (for some topics far more important than) the resources above!¹

Ahrens (2024), “Robust Causal Inference using Double/Debiased Machine Learning: A Guide for Empirical Research”, MZES Methods Bites Seminar Link

Here I literally added it to the resources folder before I realized that it has a discussion of a paper I did during the PhD. So, perhaps leaving it in here is some sort of humble brag, but finding it originally was not! (It’s what came up for me when I searched “Double/Debiased Machine Learning” on YouTube in May 2025 😜)

Miscellaneous / Commonly-Used Books That We’re Not Really Using

King, Keohane, and Verba (1994), Designing Social Inquiry

I don’t really have a substantive critique of this book, or a deep reason for not using it, other than that I find myself getting really bored whenever I try to read it 🙈

References

Ahrens, Achim. 2024. “Robust Causal Inference Using Double/Debiased Machine Learning: A Guide for Empirical Research.”

Angrist, Joshua D., and Jörn-Steffen Pischke. 2014. Mastering ’Metrics: The Path from Cause to Effect. Princeton University Press.

Barron, Alexander T. J., Jenny Huang, Rebecca L. Spang, and Simon DeDeo. 2018. “Individuals, Institutions, and Innovation in the Debates of the French Revolution.” Proceedings of the National Academy of Sciences 115 (18): 4607–12.

Björkegren, Daniel, Joshua E. Blumenstock, and Samsun Knight. 2025. “What Do Policies Value?” Review of Economic Studies.

Blaydes, Lisa, Justin Grimmer, and Alison McQueen. 2018. “Mirrors for Princes and Sultans: Advice on the Art of Governance in the Medieval Christian and Islamic Worlds.” The Journal of Politics 80 (4): 1150–67.

Gelman, Andrew, and Jennifer Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

Hume, David. 1748. An Enquiry Concerning Human Understanding. Hackett Publishing.

Kalyvas, Stathis N. 2006. The Logic of Violence in Civil War. Cambridge University Press.

King, Gary, Robert Owen Keohane, and Sidney Verba. 1994. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton University Press.

Koller, Daphne, and Nir Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press.

Morgan, Stephen L., and Christopher Winship. 2015. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press.

Pearl, Judea. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press.

Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic Books.

Sperber, Dan. 1996. Explaining Culture: A Naturalistic Approach. Cambridge: Blackwell.

Footnotes

1. For similar reasons, audiobooks may provide more effective ways to digest some topics in the course!