
Cause and Effect: Revolutionary New Statistical Test That Can Tease Them Apart - signa11
https://medium.com/the-physics-arxiv-blog/cause-and-effect-the-revolutionary-new-statistical-test-that-can-tease-them-apart-ed84a988e#.96bxatfnv
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unimpressive
Link to paper:
[http://arxiv.org/pdf/1412.3773v3.pdf](http://arxiv.org/pdf/1412.3773v3.pdf)

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lazyjeff
Seems like an extension of work by Judea Pearl (the graphical models in the
blog's first figure is a dead giveaway), which requires knowing certain
assumptions about the data. Not sure I'd call it "revolutionary" as there is a
ton of work in this direction (much of it cited in the pre-print).

Specifically, note from the pre-print this limitation, so it's not really the
causality that most people have in mind when they think they have proven cause
and effect (intervention is still the only way to really determine cause and
effect without redefining causality or making tricky assumptions about the
underlying causal model):

In this work, we will simplify matters considerably by considering only (a)
and (b) in Figure 2 as possibilities. In other words, we assume that X and Y
are dependent (i.e., PX,Y 6= PXPY ), there is no confounding (common cause of
X and Y ), no selection bias (common effect of X and Y that is implicitly
conditioned on), and no feedback between X and Y (a two-way causal
relationship between X and Y ). Inferring the causal direction between X and Y
, i.e., deciding which of the two cases (a) and (b) holds, using only the
observational distribution PX,Y is the challenging task that we consider in
this work.

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sjg007
so two obs variables that are indistinguishable by the additive noise model
must either be the same or have a hidden cause.

