Causal Inference in Statistics: A Primer
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Causal Inference in Statistics: A Primer
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Re: Causal Inference in Statistics: A Primer
#2I was looking for a review, and found Gelman's recommendation of
Causal Inference for Statistics, Social, and Biomedical Sciences
and mention of this book: http://www.hsph.harvard.edu/miguel-hernan/causal-inference-b...
Does anyone have a comment on those? I've read Pearl's two earlier books, and found the one on causality quite hard to navigate. The basic ideas are cool, but it's hard to connect the more advanced theorems with anything I could actually implement.
Re: Causal Inference in Statistics: A Primer
#3Judea Pearl's work on causality is some of the most important statistics work that is happening these days. We've known how to do statistics to find correlations and make inferences, but he put causality on a firm mathematical basis, and discovered fascinating statistics as he did. This book should be a blast.
Re: Causal Inference in Statistics: A Primer
#4Philosophy 101 would tell us that statistics could capture only correlations, not causation. Causation require different kind of knowledge, of what is beyond appearances.
Re: Causal Inference in Statistics: A Primer
#5Philosophy 101 would tell us that statistics could capture only correlations, not causation. Causation require different kind of knowledge, of what is beyond appearances.
When we see that two events are correlated (which we need some kind of statistics to do), we can tell a story (a theory, or an explanation) about how one event causes the other. If the explanation stands up to rational testing over time (where statistics are an important tool), then we have gained knowledge - one plausible explanation of what is "beyond appearances".
Therefore statistics are useful both before positing an explanation, and after to falsify it.
Re: Causal Inference in Statistics: A Primer
#6I was looking for a review, and found Gelman's recommendation of Causal Inference for Statistics, Social, and Biomedical Sciences and mention of this book: http://www.hsph.harvard.edu/miguel-hernan/causal-inference-b... Does anyone have a comment on those? I've read Pearl's two earlier books, and found the one on causality quite hard to navigate. The basic ideas are cool, but it's hard to connect the more advanced th…
They're both great books, but they make heavier use of potential outcome notation and focus less on graph-theoretic formulations than Pearl's work.
Re: Causal Inference in Statistics: A Primer
#7Philosophy 101 would tell us that statistics could capture only correlations, not causation. Causation require different kind of knowledge, of what is beyond appearances.
When we see that two events are correlated (which we need some kind of statistics to do), we can tell a story (a theory, or an explanation) about how one event causes the other. If the explanation stands up to rational testing over time (where statistics are an important tool), then we have gained knowledge - one plausible explanation of what is "beyond appearances". Therefore statistics are useful both before positi…
That theory or explanation requires the domain knowledge I am talking about.
Mere statistics about appearances is not enough.
To make it clear - statistics is obviously useful. It just cannot infer any proposition like x is y for all values of x.
Re: Causal Inference in Statistics: A Primer
#8I was looking for a review, and found Gelman's recommendation of Causal Inference for Statistics, Social, and Biomedical Sciences and mention of this book: http://www.hsph.harvard.edu/miguel-hernan/causal-inference-b... Does anyone have a comment on those? I've read Pearl's two earlier books, and found the one on causality quite hard to navigate. The basic ideas are cool, but it's hard to connect the more advanced th…
I too found Pearl's book hard to navigate on first attempt. Do not let that stop you! After a hiatus, I stumbled upon this blog post [1], which explained the core ideas in Pearl's framework beautifully in a simple language. My advice is to persist, fill any holes in fundamentals (mostly basic probability), and persist. After working out the examples in the blog post on paper and contrasting it to other ideas out there (potential outcome framework), it became quite clear what Pearl was trying to articulate.
Pearl is also an enthusiastic speaker. You can search for his talks online at various venues (Stanford, Microsoft Research, etc.) to learn more.
[1] http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...
Re: Causal Inference in Statistics: A Primer
#9I was looking for a review, and found Gelman's recommendation of Causal Inference for Statistics, Social, and Biomedical Sciences and mention of this book: http://www.hsph.harvard.edu/miguel-hernan/causal-inference-b... Does anyone have a comment on those? I've read Pearl's two earlier books, and found the one on causality quite hard to navigate. The basic ideas are cool, but it's hard to connect the more advanced th…
Gelman's own book (with Jennifer Hill) also has some good, practical techniques on causal inference. [0]
Morgan and Winship's Counterfactuals and Causal Inference: Methods and Principles for Social Research [1] is also really good. Be sure to get the second edition; it's much better than the first.
[0] http://www.amazon.com/Analysis-Regression-Multilevel-Hierarc...
[1] http://www.amazon.com/Counterfactuals-Causal-Inference-Princ...
Re: Causal Inference in Statistics: A Primer
#10Earlier quoted context omitted.
When we see that two events are correlated (which we need some kind of statistics to do), we can tell a story (a theory, or an explanation) about how one event causes the other. If the explanation stands up to rational testing over time (where statistics are an important tool), then we have gained knowledge - one plausible explanation of what is "beyond appearances". Therefore statistics are useful both before positi…
That theory or explanation requires the domain knowledge I am talking about. Mere statistics about appearances is not enough. To make it clear - statistics is obviously useful. It just cannot infer any proposition like x is y for all values of x.
I agree. I should have said that explicitly, before.