Results 41 to 50 of about 110 (98)

Variable importance for causal forests: breaking down the heterogeneity of treatment effects

Journal of Causal Inference
Causal random forests provide efficient estimates of heterogeneous treatment effects. However, forest algorithms are also well-known for their black-box nature, and therefore, do not characterize how input variables are involved in treatment effect ...
Bénard Clément, Josse Julie
doaj   +1 more source

Lower and upper bounds for the eigenvalues of a pentadiagonal matrix arising in the Hodrick-Prescott filter

Special Matrices
This study establishes lower and upper bounds for the eigenvalues of a symmetric pentadiagonal matrix arising in the Hodrick-Prescott (HP) filter, a widely used method for trend extraction in macroeconometrics.
Yamada Hiroshi
doaj   +1 more source

Optimal bandwidth estimators of kernel density functionals for contaminated data. [PDF]

J Appl Stat, 2021
Gündüz N, Aydın C.
europepmc   +1 more source

Some theoretical foundations for the design and analysis of randomized experiments

Journal of Causal Inference
Neyman’s seminal work in 1923 has been a milestone in statistics over the century, which has motivated many fundamental statistical concepts and methodology.
Shi Lei, Li Xinran
doaj   +1 more source

Personalized treatment selection using observational data. [PDF]

J Appl Stat, 2023
Kulasekera KB, Tholkage S, Kong M.
europepmc   +1 more source

Level sets of depth measures in abstract spaces. [PDF]

Test (Madr), 2023
Cholaquidis A, Fraiman R, Moreno L.
europepmc   +1 more source

Bayesian inference in based-kernel regression: comparison of count data of condition factor of fish in pond systems. [PDF]

J Appl Stat, 2022
Senga Kiessé T, Rivot E, Jaeger C, Aubin J.   +3 more
europepmc   +1 more source

Conservative inference for counterfactuals

Journal of Causal Inference
In causal inference, the joint law of a set of counterfactual random variables is generally not identified. But many interesting quantities are functions of the joint distribution.
Balakrishnan Sivaraman, Kennedy Edward, Wasserman Larry   +2 more
doaj   +1 more source

Regression in tensor product spaces by the method of sieves. [PDF]

Electron J Stat, 2023
Zhang T, Simon N.
europepmc   +1 more source

A dependent counting INAR model with serially dependent innovation. [PDF]

J Appl Stat, 2021
Shirozhan M, Mohammadpour M.
europepmc   +1 more source