Citation Analysis

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
Sushant More, Priya Kotwal, Sujith Chappidi, Dinesh Mandalapu, Chris Khawand
https://arxiv.org/abs/2409.02332

Citation mentions

Cited references

Sections

2,012

Words (approx)

References by Citation Intensity

Ordered by composite index (descending). Higher values indicate more intensive citation.

#	Reference	Year	Mentions	Breadth	Sec. Wtd	Share	Composite	Main %
1			3	2	5.0	0.188	0.737	100%
2			1	1	2.0	0.062	0.406	100%
3	Neyman, Jerzy. Sur les applications de la theorie ...	1923	1	1	2.0	0.062	0.406	100%
4			1	1	2.0	0.062	0.406	100%
5			1	1	2.0	0.062	0.406	100%
6			1	1	2.0	0.062	0.406	100%
7			1	1	2.0	0.062	0.406	100%
8			1	1	2.0	0.062	0.406	100%
9	Chernozhukov, V., Goldman, M., Semenova, V., and T...	2017	1	1	2.0	0.062	0.406	100%
10	Huber, Peter J. The behavior of maximum likelihood...	1967	1	1	2.0	0.062	0.406	100%
11			1	1	2.0	0.062	0.406	100%
12	A. Abadie and G. Imbens, On the failure of bootstr...	2008	1	1	2.0	0.062	0.406	100%
13	Chernozhukov, Victor, Mert Demirer, Esther Duflo, ...	2022	1	1	2.0	0.062	0.406	100%
14	Knaus, M. C., Lechner, M., & Strittmatter, A	2021	1	1	2.0	0.062	0.406	100%

Measures: Mentions = total in-text citations; Breadth = distinct sections; Sec. Wtd = section-weighted count (body ×2, lit review/appendix ×0.5); Share = mentions / total citations in paper; Composite = geometric mean of normalised count, breadth, and main-text ratio; Main % = fraction of mentions in main text (excl. appendix). (self) = self-citation.