Md. Asif Uddin

    Chapter 4 VI.4

    Identification

    Identification asks whether an interventional quantity can be written in terms of observed ones, and the graph answers it.

    Whether an interventional quantity can be written in observed ones.

    How this chapter is built

    M3Load-bearing

    The content is mathematics. Understanding is demonstrated by computation, not recall.

    basics1/11what the words mean
    concept2/2what to picture
    theory0/4why it works, and when it does not
    mathematics0/15derive it, then compute it
    practice0/9build it, break it, read the papers

    Five strands, not one. Mathematics is the spine; the other four are the body. A chapter cannot pay its way out of teaching with problems, nor out of problems with teaching.

    Before you start

    The problem

    An experiment is often impossible. The question then is whether the effect is recoverable from observation at all — a question with a yes-or-no answer that depends on the graph and not on the sample size.

    One encoder stage, before and afterThe V1 encoder stage is two Swin transformer blocks followed by patch merging. V2 prepends a residual convolution to every stage. That single block is the entire contribution of the paper.Swin UNETRSwin UNETR V2Swin blockSwin blockpatch mergingresidual convSwin blockSwin blockpatch mergingin MONAI this is use_v2=TrueSelf-attention has no stronginductive bias, so the model isharder to train and hungrierfor data — their own abstract.
    Fig. 4 — One residual convolution at the head of every encoder stage. That single block is the entire contribution of the paper.

    What this chapter covers

    • Backdoor criterion
    • Adjustment
    • Front-door criterion
    • Conditional independence

    Apparatus

    The mathematics this chapter leans on, held in Book 0 so it can be assumed here without being taught here. Not a gate — follow a link when a step stops making sense.

    Bayes' rule 0.PR.04 · Conditional independence 0.PR.05 · Estimators, bias and variance 0.ST.01

    Notation

    • do(·)The intervention operator
    • X (random)A random variable
    • 𝔼Expectation
    • VarVariance

    Propositions

    Not yet written. The topics above are the plan for this chapter; each will become a proposition with its own figure.

    Worked problems

    0/5 problems0/4 variants0/10 exercisesowes 15 more

    Not yet written. At M3 this chapter owes 5 worked problems across 4 distinct variants, and 10 exercises, every one with a published solution. The build enforces that from the day the chapter is marked published.