Difference-in-differences with compositional changes

PHC Sant'Anna, Q Xu - arxiv preprint arxiv:2304.13925, 2023 - arxiv.org
This paper studies difference-in-differences (DiD) setups with repeated cross-sectional data
and potential compositional changes across time periods. We begin our analysis by deriving …

Efficient difference-in-differences estimation with high-dimensional common trend confounding

M Zimmert - arxiv preprint arxiv:1809.01643, 2018 - arxiv.org
This study considers various semiparametric difference-in-differences models under
different assumptions on the relation between the treatment group identifier, time and …

A Mann–Whitney test of distributional effects in a multivalued treatment

C Ai, L Huang, Z Zhang - Journal of Statistical Planning and Inference, 2020 - Elsevier
This article considers a Mann–Whitney test of distributional effects in a multivalued
treatment. Specifically, we first show that, under the unconfoundedness condition, the …

Estimating causal effects under non-individualistic treatments due to network entanglement

P Toulis, A Volfovsky, EM Airoldi - Biometrika, 2025 - academic.oup.com
In many observational studies, the treatment assignment mechanism is not individualistic, as
it allows the probability of treatment of a unit to depend on quantities beyond the unit's …

Tuning-parameter-free propensity score matching approach for causal inference under shape restriction

Y Liu, J Qin - Journal of Econometrics, 2024 - Elsevier
Propensity score matching (PSM) is a pseudo-experimental method that uses statistical
techniques to construct an artificial control group by matching each treated unit with one or …

Partial mean processes with generated regressors: Continuous treatment effects and nonseparable models

YY Lee - arxiv preprint arxiv:1811.00157, 2018 - arxiv.org
Partial mean with generated regressors arises in several econometric problems, such as the
distribution of potential outcomes with continuous treatments and the quantile structural …

Causal inference of general treatment effects using neural networks with a diverging number of confounders

X Chen, Y Liu, S Ma, Z Zhang - Journal of Econometrics, 2024 - Elsevier
Semiparametric efficient estimation of various multi-valued causal effects, including quantile
treatment effects, is important in economic, biomedical, and other social sciences. Under the …

[HTML][HTML] Formal finance usage and innovative SMEs: Evidence from ASEAN countries

M Arif, M Hasan, A Shafique Joyo, C Gan… - Journal of Risk and …, 2020 - mdpi.com
This paper provides evidence on the likelihood of formal finance usage among innovative
small and medium enterprises (SMEs) operating in ASEAN countries. To this end, the SMEs …

Multivalued treatments and decomposition analysis: An application to the WIA program

W Ao, S Calonico, YY Lee - Journal of Business & Economic …, 2021 - Taylor & Francis
This article provides a general estimation and inference framework to study how different
levels of program participation affect participants' outcomes. We decompose differences in …

Propensity score methodology in the presence of network entanglement between treatments

P Toulis, A Volfovsky, EM Airoldi - arxiv preprint arxiv:1801.07310, 2018 - arxiv.org
In experimental design and causal inference, it may happen that the treatment is not defined
on individual experimental units, but rather on pairs or, more generally, on groups of units …