Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Placebo inference on treatment effects when the number of clusters is
  small   I introduce a general, Fisher-style randomization testing framework to
conduct nearly exact inference about the lack of effect of a binary treatment
in the presence of very few, large clusters when the treatment effect is
identified across clusters. The proposed randomization test formalizes and
extends the intuitive notion of generating null distributions by assigning
placebo treatments to untreated clusters. I show that under simple and easily
verifiable conditions, the placebo test leads to asymptotically valid inference
in a very large class of empirically relevant models. Examples discussed
explicitly are (i) least squares regression with cluster-level treatment, (ii)
difference-in-differences estimation, and (iii) binary choice models with
cluster-level treatment. A simulation study and an empirical example are
provided. The proposed inference procedure is easy to implement and performs
well with as few as three treated and three untreated clusters.

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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas