Discover and read the best of Twitter Threads about #BookofWhy

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I summarize this discussion thus: Progress in our
century amounts to building computational models of mental processes that have escaped scrutiny under slippery terms such as "design" "discovery" "background knowledge"
"familiarity" "fieldwork". etc. Saying "DAGs are not enough"
2/ produces no progress until one is prepared to propose a mathematical model for what IS enough. The IV criterion I presented is necessary for justification, so keep it in mind and teach it to natural experimentalists who should be jubilant seeing their IV's justified. 2/3
3/3 We should always be open to extensions and refinements, but mystification holds us back. We had enough of that in the 20th century. #Bookofwhy
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"How to extend causal inferences from a RTC to
a target population?" I am retweeting this thread since many readers ask this question, and many know of the comprehensive solution described in and in
using graphical models. 1/2
I was surprised therefore to read Miguel's proposal to recast the whole question in the opaque language of PO, where assumptions are so far removed from scientific understanding. A face-to-face comparison of the two approaches is given here:
The thread itself, including Miguel's proposal, can be found here
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1/ I am retweeting, because the question: "Compare the PO vs. DAG approaches on applied, 'real-life' data" comes up once a week. Here it is: Take any applied paper labeled "PO" and re-label it "DAG" after adding to it three tiny ingredients: (1) Show how the modeling assumptions
2/ emerge organically from our scientific understanding of the problem, (2) Show if there is anything we can do if it doesn't thus emerge, and (3) test whether those assumptions are compatible with the available data. Once added, Bingo! We have an "applied DAG" approach,
3/3 demonstrated on "real life" data, that can be compared to the "applied PO approach" or "applied quasi-experimental approach" on all counts, especially on: (1) transparency, (2) Power, and (3) Testability. #Bookofwhy
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1/3 This is an excellent paper, that every regression analyst should read. Primarily, to appreciate how problems that have lingered in decades of confusion can be untangled today using CI tools. What I learned from it was that the "suppressor surprise" is surprising even when
2/3 cast in a purely predictive context: "How can adding a second lousy predictor make the first a better predictor?" Evidently, what people expect from predictors clashes with the logic of regression slopes. The explanation I offered here (Section 3)
3/3 shows how the phenomenon comes about, but the reason for the clash is still puzzling: What exactly do people expect from predictors, and why? #Bookofwhy
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1/ Continuing our exploration of "Reduced Form Equations" (RFE) and what they mean to economists, let me address some hard questions that CI analysts frequently ask. Q1: Isn't a RFE just a regression equation? A1. Absolutely Not! A RFE carries causal information, a regression
2/ equation does not. Q2: Isn't a RFE just a structural equation? A1. No! Although a RFE carries causal information (much like a structural equation) the RFE may not appear as such in the structural model; it is derived from many such equations though functional composition.
3/ (The output-instrument in the IV setting is a typical example). Q3: One may derive many equations from a structural model; what makes a RFE so special to deserve its own name. A3: It is exceptional because it comes with a license of identification by OLS. This is not usually
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1/3 Econ. readers asked if they can get hold of those magical night-vision goggles that tell us which causal effects in an econ. model are identifiable by OLS (and how). The answer is embarrassingly simple: Consider
Model 2 in p. 163 of
2/3 Take any two variables, say Z_3 and Y. If you can
find a set S of observed variables (e.g., S={W_1,W_2}),
non-descendants of Z_3, that block all back doors paths from Z_3 to Y, you are done; the coefficient a in the regression Y = a*Z_3 + b1*W_1 +b2*W_2
gives you the right
3/3 answer. A similar goggle works for a single
structural parameter (See page 84 of Primer Duck soup. This re-begs the question whether the restricted notion of "reduced form" is still needed in 2019. @EconBookClub @lewbel #Bookofwhy.
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1/ Alright folks, this will be the last round of #EconBookClub, discussing Guido Imbens' new paper on PO vs. DAGs (link: #EconTwitter #BookofWhy #Econometrics #Causality #CausalInference #MachineLearning #AI
2/ Today I will go through the final chapter 4.6 and the paper's conclusion. Maybe you will notice that I skipped over section 4.5. I had some thoughts on this chapter, but I feel that I didn't understand Guido's point well enough. So I prefer to leave it aside for the moment.
3/ In chapter 4.6, Guido discusses the returns to education example, which is a classic question in economics and which is also closely connected with the development of PO techniques in econometrics.
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1/4 I would like to welcome the 500 new followers who
have joined us on Tweeter since @SamHarrisOrg posted our conversation on his podcast. Welcome to the wonderful land of WHY and, please, be aware of what you are getting yourself into. Our main theme is the #Bookofwhy and
2/4 the way it attempts to democratize the science of cause and effect and apply it in artificial intelligence, philosophy, and the health and social sciences, see We alert each other to new advances in causal reasoning and new methods of answering causal
3/4 questions when all we have are data, assumptions and the logic of causation. We also debate detractors and nitpickers who mistrust fire descending from Mt. Olympus for use by ordinary mortals. I spend time on such debates knowing that for every nitpicker there are dozens of
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2/ In this chapter, Guido responds to the main criticism that is brought forward towards the potential outcomes framework by proponents of DAGs – the choice of covariates or how to justify ignorability.
3/ We're all familiar with the ignorability / unconfoundedness assumption that underlies classical matching estimators, for example. The corresponding DAG is depcited in Figure 8a of the paper.
Read 17 tweets
I have just posted my 3rd comment on Lord Paradox…. Here, I returned to my original goal of empowering readers with an understanding of the origin of the paradox and what we can learn from it. Red herrings have been taking too much of our time. #Bookofwhy
It is important to add that the huge literature on Simpson's and Lord's paradoxes attests to a century of scientific frustration with a simple causal problem, deeply entrenched in out intuition, yet helplessly begging for a formal language to get resolved. I can count dozens of
red herrings thrown at this stubborn problem, to deflect attention from its obvious resolution. Why? Because the latter requires the acceptance of a new language - the hardest transition for adults. I am grateful for the opportunity to talk to thousands of young followers here
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Any discussion of "paradoxes" is really an exercise in psychology. Yet we, quantitative analysts, are trying to avoid psychology at all cost. We can't. We must explicate why two strong intuitions seem to clash, and the conditions under which our intuitions fail. See #Bookofwhy
That is why I am begging folks: "Please, do not tell me 'I am not entirely satisfied' before you tell me why you are surprised (by the paradox) ". I am proud that #Bookofwhy addresses this question (of "surprise") head on, before offering "a resolution".
Now, speaking specifically about Lord's paradox, the paradox was introduced to us in "asymptotic" terms (ie, using distributions, not samples) and we were surprised. Is it likely that we can resolve our surprise by going to finite samples? or to "block design"? #Bookofwhy
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1/ Let's continue with chapter 4.3 of Guido Imbens' new working paper on PO versus DAGs (link:…), this time with a discussion about simultaneity and cyclic models. #EconBookClub #BookofWhy #Econometrics #Causality #MachineLearning
2/ The chapter raises an important point. Many canonical models in economics are cyclic equilibrium models (in our language, we would call them "nonrecursive"). And DAGs are by definition acyclic, so they cannot really capture such models. But...
3/ The chapter follows a line of argument that is very similar to the last section, and which I don't agree with. It focusses on a special case (here a simple supply-and-demand equilibrium model) that lies outside the realm of DAGs, and which was solved by PO practitioners.
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1/ Alright, today in #EconBookClub I'd like to discuss chapter 4.2 of Guido Imbens' new working paper on the differences between the potential outcomes framework and directed acyclic graphs (link:…). #BookofWhy #Causality #Econometrics
2/ This chapter is about instrumental variable estimation, a topic to which Guido has contributed tremendously. Imbens and Angrist (1994) was definitely a big milestone in econometrics. I agree less with Guido's opinions on DAGs in this context though—as you might expect by now.
3/ Working with a graphical representation of a structural causal model (SCM) – i.e. the DAG – means that we explicitly refrain from making any distributional or functional form assumptions. That's a feature, not a bug!
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1/ I am recommending this paper to every data analyst educated by traditional textbooks, which start with
regression equations, add and delete regressors, estimate and compare coefficients before and after
deletion, and then ask which coefficient has "causal interpretation"
2/ I was shocked to realize that the majority of data analysts today are products of this culture, trapped in endless confusion, with little chance to snap out of it, since journal editors, reviewers and hiring committees
are trapped in the same culture. The new PO framework does
3/ offer a theoretical escape route from this culture, through the assumption of "conditional ignorability" but, since it is congnitively formidable, practicing analysts must rely on regression arguments.
Keele et al examine a causal model (Fig.2) and ask: suppose we regress Y
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1/ Let's go for another round of #EconBookClub! By now, we've reached the most important chapter 4 of Guido Imbens' new paper in which he contrasts DAGs with the potential outcomes (PO) framework (link:…) #BookofWhy #Causality #Econometrics
2/ This chapter has six subsections and because my previous threads were already quite long, I decided to go thorugh them one-by-one in the following days.
3/ Section 4.1 is about the role of manipulability in causal inference. Something that has been discussed here a lot recently by the epidemiologists and also @yudapearl commented on it a while ago.
Read 15 tweets
1/ It's #EconBookClub time again. Today, I will focus on chapter 3 of Guido Imbens' new paper, in which he compares the potential outcome framework with graphical approaches to #causation. #BookofWhy #DAG #Causality #AI #Econometrics
2/ After having introduced his readers to the fundamentals of DAGs, Guido now constrasts them with the potential outcomes framework, to which he contributed massively in his work within econometrics.
3/ Again, this chapter of the paper is mostly definitional, so I only have a couple minor comments. But I would like to pick up some of the threads that the paperleaves open in certain places, in my view.
Read 23 tweets
1/ I just read… and I agree with @suchisaria. Anyone concerned with stability (or invariance) should start with this paper to get a definition of we are looking for and why. Arjovsky etal paper should be read with this perspective in mind, as an attempt to
2/ secure this sort of stability w/o having a model, but having a collection of varying datasets instead. The former informs us when the latter's attempts will succeed or fail. It is appropriate here to repeat my old slogan: "It is only by taking models seriously that we learn
3/ when they are not needed". I wish I could quote from Aristotle but, somehow, the Greeks did not argue with their Babylonian rivals, the curve-fitters. They just went ahead and measured the radius of the earth AS IF their model was correct, and the earth was round. #Bookofwhy
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1/ Alright, time for round 3 of #EconBookClub! In this iteration I will cover chapters 2.4 to 2.9 of Guido Imbens' new working paper on PO vs. #DAGs (link:…). #BookofWhy #Causality #AI #MachineLearning
2/ The Twitter thread will probably be much shorter this time compared to the last ones, simply because Guido discusses a lot of terminology and technical details in these section, which I don't have much to comment on.
3/ But I'm extremely happy that Guido provides an introduction into the basics of DAGs and calculus to a broader audience in economics. Finally, I have a paper to cite when I use the terms "confounder" or "mediator", since this type of terminology,
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1/ It's again time for #EconBookClub! Let's continue our discussion of Guido Imbens' new paper on DAGs and PO. But first of all, let me thank all of you who were engaging with the first thread. It was really amazing to see all the reactions. Thanks!
2/ Alright, I managed to read sections 2.1 to 2.3 today, in which Guido reviews some parts of the #BookofWhy and provides an introduction into the fundamental ideas behind Directed Acyclic Graphs (DAGs) for a broader econometric audience.
3/ Guido is exactly right with his remark here, that a large focus of the causal inference literature in computer science is on identification. To understand this specific angle, you have to look at how CS folks approach the problem of causal inference.
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I'm very much looking forward to read Imben's new piece on PO and DAGs. Inspired by @EpiEllie's #EpiBookClub, I'd like to start something similar to delve deeper into the individual sections of the paper. So let's start #EconBookClub! #BookofWhy 1/
2/ After having read the introduction, it's very cool to see that Guido apparently had a change of mind with respect to the usefulness of DAGs. Compare this passage of the paper with his 2014 paper published in Statistical Science:…
3/ It's not exactly clear what Guido's personal stance is, but there's certainly a change in tone here. That's great, because it means that the paper could be a real starting point for having an objective discussion about how DAGs can add to our econometric toolbox.
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1/3 While struggling to find a ladder or demarcation lines between rungs, Hand's paper called my attention to a comprehensive article by Shmueli (2010) "To Explain or to Predict?", rich with references and quotes, which states: "Although not explicitly stated in the methodology
2/2 literature, applied statisticians instinctively sense that predicting and explaining are different."…
Going through the quotes and references in Hand's paper, I believe they confirm Shmueli's verdict: "the statistical literature lacks a thorough
3/3 discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal." One may also note that, adding to a "thorough" discussion, the Ladder of Causation provides "formal distinctions" between those differences. #Bookofwhy
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1/4 Happy Anniversary! About a year ago, I started Twitting and posted this:
6.27.18 - Hi everybody, the intense discussion over The Book of Why
drove me to add my two cents. I will not be able to comment on every
tweet, but I will try to squeak where it makes a difference....
2/4 A year later, I can hardly believe the 2,300 Tweets behind me, 19.1K followers, and a pleasant sense of comradeship with the many inquisitive minds that have been helping me demystify the science of cause and effect. I have benefited immensely from seeing how causality is
3/4 bouncing from your angle and how it should be presented to improve on past faults. To celebrate our anniversary, I am providing a link to a search-able file with all our conversations (my voice) sorted chronologically… Today I re-read selected sections
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1/3 Economists' "statistical discrimination," as it turns out, is both (1) the use of statistical associations to discriminate and (2) an attempt to define discrimination using statistical vocabulary alone. According to Phelps (1972), you discriminate whether you hire people by
2/3 education, race or zip code, as long as you base decisions on PREDICTED performance, rather than performance itself. So, all learning is
"discriminatory" for it is based on past experience which PREDICTS, yet is not equal, the situation at hand. Conclusion: I was right
3/3 to suspect criteria entitled "statistical discrimination" and their ability to capture notions such as "fairness," in which causal relations play a major role. @JaapAbbring @steventberry #Bookofwhy
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1/3 If by "statistical discrimination" we mean the use of statistical associations in the decision, then it is perfectly harmonious with the causal definition of "discrimination" and I see nothing wrong with it. What makes me suspicious are attempts to DEFINE discrimination by
2/3 statistical criteria. Note that in your example, causal considerations are unavoidable, for if the observed characteristics causally produce/prevent necessary skills, our notion of discrimination would change. This sensitivity to causal relations is absent from the fairness
3/3 literature I have sampled thus far, and which seems to dominate recent discussions in ML. Note also that we now have the tools to define and manage criterion based on combined statistical+causal relations. BTW, the link you gave us is blocked. #Bookofwhy
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