Online resources for philosophy of causation for causal inference Can you recommend any books, articles, essays, online tutorials/courses, etc that would be interesting and useful for an epidemiologist/biostatistician to learn about the philosophy of causation/causal inference?
I know quite a bit about actually doing causal inference from an epi and biostats framework, but I would like to learn something about the philosophy which underlies and motivates this work. For example, it's my understanding that Hume first talked about ideas that could be interpreted as counterfactuals. 
I have basically no training or experience with philosophy, so I need something relatively introductory to start of with, but I would also be interested in recommendations for more complex but important/foundational texts/authors (but please indicate that they are not introductory). 
I hope this isn't too off-topic for cross-validated, but I'm hoping that some of you will have been in the same boat as me before and able to share your favorite resources.
 A: Without wanting to delve into specific papers, I think an excellent resource for something like that would be the Stanford Encyclopedia of Philosophy. The lemmas on  Probabilistic Causation and Causation and Manipulability are peer reviewed, meticulous annotated and give great pointers on where to focus your research next.
Just to cite and two papers: Two extremely enjoyable articles on the matter are The Unreasonable Effectiveness of Mathematics in the Natural Sciences by Wigner (1960) and (lighter and definitely more recent) The Unreasonable Effectiveness of Data by Halevy, Norvig, and Pereira (2009).
A: @user11852 is putting you on the right track.  Let me add Pearl's lecture on The Art and Science of Cause and Effect as a light and fun introduction to the ideas / history and philosophy of the topic.  
A: With Philosophy, a good starting point is always the work of Bertrand Russell. There's no doubt that you'd find sections in Russell's History of Western Philosophy that cover the philosophy of causation/causal inference, but given its size and broad scope, it would be difficult for me to pin down for you exactly where to look in this book. Taking the longer term view, though, this is the book to start with if you want to deepen your knowledge of philosophy - its evolution - and philosophers themselves.
A second book by Bertrand Russell that's worth consulting is Human Knowledge. Part V of this book covers Probability while Part VI is about the Postulates of Scientific Inference. Both of these topics are discussed from the philosopher's standpoint. To give you a taste of the book, I've added two extracts from the Introduction below.
In the Introduction to the book, Bertrand tells us a little bit about Part V Probability:

Since it is admitted that scientific inferences, as  rule, only confer
  probability on the conclusions, Part V proceeds to the examination of
  Probability. This term is capable of various interpretations , and has
  been differently defined by different authors. These interpretations
  and definitions are examined, and so are the attempts to connect
  induction with probability. In this matter the conclusion reached is,
  in the main, that advocated by Keynes: that inductions do not make
  their conclusions probable unless certain conditions are fulfilled, and that
  experience alone can never prove that these conditions are fulfilled.

And on Part VI Postulates of Scientific Inference, Bertrand says (again, from the Introduction):

Part VI, on the postulates of scientific inference, endeavours to
  discover what are the minimum assumptions, anterior to experience,
  that are required to justify us in inferring laws from a collection of
  data; and further, to inquire in what sense, if any, we can be said to
  know that these assumptions are valid. The main logical function that
  the assumptions have to fulfill is that of conferring a high
  probability on the conclusions and inductions that satisfy certain
  conditions. For this purpose, since only probability is in question,
  we do not need to assume that such-and-such a connection of events
  occurs always, but only that it occurs frequently. For example, one of the 
  assumptions that appear necessary is that of separable causal chains,
  such as are exhibited by light-rays or sound-waves. This assumption
  can be stated as follows: when an event having a complex space-time
  structure occurs, it frequently happens that it is one of a train of
  events having the same or a very similar structure. (A more exact
  statement will be found in Chapter 6 of this Part.) This is part of a
  wider assumption of regularity, or natural law, which, however,
  requires to be stated in more specific forms than is usual, for in its
  usual form it turns out to be a tautology.
That scientific inference requires, for its validity, principles which
  experience cannot render even probable, is, I believe, an inescapable
  conclusion from the logic of probability. For empiricism, it is an
  awkward conclusion.
But I think it can be rendered somewhat more palatable by the analysis of the 
  concept of "knowledge" undertaken in Part II. "Knowledge", in my
  opinion, is a much less precise concept than is generally thought, and
  has its roots more deeply embedded in unverbalized animal behaviour
  than most philosophers have been willing to admit. The logically basic
  assumptions to which our analysis leads us are psychologically the end
  of a long series of refinements which start from habits of expectation
  in animals, such as that what has a certain kind of smell will be good
  to eat. To ask, therefore, whether we "know" the postulates of
  scientific inference, is not so definite a question as it seems. The
  answer must be: in one sense, yes, in another sense, no; but in the
  sense in which "no" is the right answer we know nothing whatever, and
  "knowledge" in this sense is a delusive vision. The perplexities of
  philosophers are due, in a large measure, to their unwillingness to
  awaken from this blissful dream.

If you decide to take things further (down the academic line), I'd also suggest searching "causal inference" in the Oxford Journal Mind. There is a search tool on the Journal's website.
A: From the title it does not sound like it but the book "Mostly Harmless Econometrics" by Angrist and Pischke gives a thorough explanation of causal effects estimation, the underlying rationale and a wide discussion of techniques useful for applied worked. They explain all techniques and their basic ideas with real-life examples, though the majority is related to economics if you don't mind that.
If you would like to have a more technical treatment of the idea of counterfactuals, a major paper in this respect is by Angrist, Imbens and Rubin (1996) in the Journal of the American Statistical Association. In there they establish a causal effects framework built on counterfactuals which uses instrumental variables to identify local average treatment effects.
