Cover Image

On the admissibility of Horvitz-Thompson estimator for estimating causal effects under network interference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Karwa, Vishesh, Airoldi, Edoardo M.
Format:	Preprint
Published:	2023
Subjects:	Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Treatment effect estimation under convergent network interference
by: Park, Bryan, et al.
Published: (2026)

The covariance of causal effect estimators for binary v-structures
by: Kuipers, Jack, et al.
Published: (2025)

Causal effect estimation under network interference with mean-field methods
by: Bhattacharya, Sohom, et al.
Published: (2024)

Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference
by: Eichhorn, Matthew, et al.
Published: (2024)

Dynamic causal inference with time series data
by: Schaffe-Odeleye, Tanique, et al.
Published: (2026)

The Proximal Robbins-Monro Method
by: Toulis, Panos, et al.
Published: (2015)

Data-adaptive exposure thresholds for the Horvitz-Thompson estimator of the Average Treatment Effect in experiments with network interference
by: Thiyageswaran, Vydhourie, et al.
Published: (2024)

Optimal estimation of generalized causal effects in cluster-randomized trials with multiple outcomes
by: Chen, Xinyuan, et al.
Published: (2026)

Non-asymptotic goodness-of-fit tests and model selection in valued stochastic blockmodels
by: Almendra-Hernández, Félix, et al.
Published: (2025)

Efficient adjustment sets for time-dependent treatment effect estimation in nonparametric causal graphical model
by: Adenyo, David, et al.
Published: (2024)

Kernel-based estimators for functional causal effects
by: Raykov, Yordan P., et al.
Published: (2025)

Inverting estimating equations for causal inference on quantiles
by: Cheng, Chao, et al.
Published: (2024)

Causal clustering: design of cluster experiments under network interference
by: Viviano, Davide, et al.
Published: (2023)

On the estimation of varextropy under complete data
by: Goodarzi, F., et al.
Published: (2024)

Sharp variance estimator and causal bootstrap in stratified randomized experiments
by: Yu, Haoyang, et al.
Published: (2024)

Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set
by: Choo, Davin, et al.
Published: (2024)

Semiparametric M-estimation with overparameterized neural networks
by: Yan, Shunxing, et al.
Published: (2025)

Optimal estimators and tests for reciprocal effects
by: Feng, Qunqiang, et al.
Published: (2026)

Efficient estimation of partially linear additive Cox models and variance estimation under shape restrictions
by: Lang, Junjun, et al.
Published: (2024)

Doubly robust estimation of causal effects for random object outcomes with continuous treatments
by: Bhattacharjee, Satarupa, et al.
Published: (2025)

Asymptotics of constrained $M$-estimation under convexity
by: Brunel, Victor-Emmanuel
Published: (2025)

Differentially private estimation in a class of directed network models
by: Pan, Lu, et al.
Published: (2022)

One‐Inflation and Zero‐Truncation Count Data Modelling Revisited With a View on Horvitz–Thompson Estimation of Population Size
by: Dankmar Böhning, et al.
Published: (2024)

Semiparametric adaptive estimation under informative sampling
by: Morikawa, Kosuke, et al.
Published: (2022)

Partial identification of principal causal effects under violations of principal ignorability
by: Wu, Minxuan, et al.
Published: (2024)

Minimax and adaptive estimation of general linear functionals under sparsity
by: Xie, Jie, et al.
Published: (2025)

Weak convergence of Bayes estimators under general loss functions
by: Requadt, Robin, et al.
Published: (2025)

On (in)consistency of M-estimators under contamination
by: Klooster, Jens, et al.
Published: (2025)

Limit results for distributed estimation of invariant subspaces in multiple networks inference and PCA
by: Zheng, Runbing, et al.
Published: (2022)

Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networks
by: Lee, Jeyong, et al.
Published: (2022)

Scale estimation and rate-unbiasedness for Gaussian processes under smoothness misspecification
by: Karvonen, Toni, et al.
Published: (2021)

Minimax density estimation in the adversarial framework under local differential privacy
by: Albert, Mélisande, et al.
Published: (2024)

Data visualization and causal reasoning are essential for causal effect estimation
by: Dan Chen, et al.
Published: (2026)

Bounding the probability of causality under ordinal outcomes
by: Sun, Hanmei, et al.
Published: (2024)

Error estimation and adaptive tuning for unregularized robust M-estimator
by: Bellec, Pierre C., et al.
Published: (2023)

Rates of convergence for density estimation with generative adversarial networks
by: Puchkin, Nikita, et al.
Published: (2021)

Non-parametric estimation of net survival under dependence between death causes
by: Laverny, Oskar, et al.
Published: (2025)

LAD estimation of locally stable SDE
by: Kulyk, Oleksii M., et al.
Published: (2026)

Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodels
by: Karwa, Vishesh, et al.
Published: (2016)

Random effects estimation in a fractional diffusion model based on continuous observations
by: Chebli, Nesrine, et al.
Published: (2024)