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Judea Pearl
@yudapearl
, 4 tweets, 1 min read Read on Twitter
1/
 In view of persistent ambiguities regarding the definition of "causal inference" (CI) I am sharing here the definition that has guided me successfully throughout my journeys. CI is a method that takes data from various sources, as well as extra-data information, and produces
2/
 answers to questions of two types (1) the effects of pending interventions and (2) the effects of hypothetical undoing of past events. See Causality (2000) Chapter 1. A vivid and recurrent example of a non-causal question is any question that can be answered from the joint
3/
 probability distribution of observed variables, eg, correlation, partial
regression, Granger causality, weak and strong endogeneity
(EHR 1983) etc. See ucla.in/2N9f28c .
This definition excludes Pearson's (1911) and Fisher's
(1925) descriptions of statistical tasks
4/4
 and I would reserve judgment on how "experimental economists" fit into this definition. I believe that, in due time, "experimental economists" will manage to articulate formally what "extra-data information" they use, and thus become bonified members of CI. #Bookofwhy
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Judea Pearl
@yudapearl
1/3
 I have read this paper with great interest, trying to understand what makes regression analysts seek the wisdom of causal diagrams when they are not asking causal questions and labor merely to assess the magnitude of measurement errors.
The answer seems to be two fold.
2/3
 (1) The diagram allows them to use Wright's Rules ucla.in/2LcpmHz to compute correlations among latent variables (X,Y) in terms of correlations among observed proxies (x',Y'). This could be done, of course, w/o the diagram, but only at the cost of painful algebraic
3/3
 derivations, as in econ. (2) The problem is in fact causal in disguise. Why else would anyone be interested in cov(X,Y) as opposed to cov(X',Y') which is
estimable from the data and is sufficient for all predictive tasks?
Curious if other readers agree. #Bookofwhy
Read 3 tweets
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Judea Pearl
@yudapearl
1/3
 Commending you on so skillfully navigating the waters of DAGs and PO. That the two are compatible comes from the fact that both are derived from structural causal models (SCM). DAGs are used to encode what we know and PO what we wish to know. However, I find it hard to
2/3
 understand why you say that "PO are most useful for estimation". Assuming that we have obtained an estimand using DAG-based identification, isn't the estimand itself sufficient for estimation? Do we really need to dress it in PO cloths before proceeding with the estimation
3/3
 phase? This dressing habit, I believe, is a remnant of bygone age when, lacking DAGs, people attempted to identify queries of interest in the PO language. But why go through that tormented experience today, when we do have DAGs.??? #Bookofwhy
Read 3 tweets
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Judea Pearl
@yudapearl
1/3
 The simplicity of IV validity quickly disappears with nuances. see But, a more important aspect of the "repackaging" is CREDIBILITY, namely, judgments are recruited from where they reside, not from where they are distorted to appease the identifier.
2/3
 Consider the IV validity again, and ask yourself "what judgements were necessary to execute this exercise?". Mark them. Now compare to the judgments required in the PO framework, which are cast in ignorability language, (See Angrist etal). Finally, ask "What type of judgments
2/3
 would be more CREDIBLE if I were the one to make them?" I am sure your assessment of the value of "repackaging" will become one of greater appreciation, perhaps even one of necessity. #Bookofwhy
Read 3 tweets

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Sunshiny
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Here is a conversation thread regarding “The Rapture.”

The threat from Dominionists is in the here and now! The plan is to take control of the 7 Mountains of influence & wait for the Rapture, which could be another 1000 years away.
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george davey smith
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“Causality is non-quantitative, causal inference is quantitative” seems a nice solution to a definitional problem, but is it true? Not if you’re interested in advancing useful knowledge or in history it isn’t
1/n “Causal inference” (CI) is a notion with its origins in Plato, Hulme et al, and used formally by thinkers as distinct as G.E. Moore and Gilbert Ryle regarding empirical (and ultimately to-be-acted-on) inference
2/n A wide range of evidence goes into CI, some quantitative, some non-quantitative. Epidemiology is one discipline that has a long tradition of struggling with what approaches serve best as a basis for practical action ncbi.nlm.nih.gov/pubmed/23171877 academic.oup.com/aje/article/17…
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@ClaraJeffery
Catch me up: when/why did everybody start saying "stan"?
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Clara Jeffery
@ClaraJeffery
1/ Amid Twitter maybe getting rid of the "like" to promote "better dialog" and certainly giving safe haven to nazis and other extremists, a rant:
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Jeremias Prassl
@JeremiasPrassl
On my way home from @ilo and @BelgiumUNGeneva high-level forum on the #sharing economy and its social challenges: a few highlights from a great discussion, moderated by @ilo senior economist Janine Berg. Short thread. 1/10 @ILO_live
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