Using double machine learning on NSW administrative data, the paper finds that legal aid denial reduces incarceration probability by about 8-10 percentage points, apparently because private lawyers are better at keeping clients out of jail, though sentenced clients serve longer spells.
Manipulation-Robust Regression Discontinuity Designs
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
We present simple low-level conditions for identification in regression discontinuity designs using a potential outcome framework for the manipulation of the running variable. Using this framework, we replace the existing identification statement with two restrictions on manipulation. Our framework highlights the critical role of the continuous density of the running variable in identification. In particular, we establish the low-level auxiliary assumption of the diagnostic density test under which the design may detect manipulation against identification and hence is manipulation-robust.
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Legal aid eligibility and court outcomes: a design-based double-machine-learning approach
Using double machine learning on NSW administrative data, the paper finds that legal aid denial reduces incarceration probability by about 8-10 percentage points, apparently because private lawyers are better at keeping clients out of jail, though sentenced clients serve longer spells.