Cover Image

Efficient estimation of longitudinal treatment effects using difference-in-differences and machine learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Illenberger, Nicholas, Díaz, Iván, Renson, Audrey
Format:	Preprint
Published:	2024
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transporting treatment effects from difference-in-differences studies
by: Renson, Audrey, et al.
Published: (2023)

What makes a study design quasi-experimental? The case of difference-in-differences
by: Renson, Audrey, et al.
Published: (2025)

Using causal diagrams to assess parallel trends in difference-in-differences studies
by: Renson, Audrey, et al.
Published: (2025)

Pulling back the curtain: the road from statistical estimand to machine-learning based estimator for epidemiologists (no wizard required)
by: Renson, Audrey, et al.
Published: (2025)

Identification and estimation of mediational effects of longitudinal modified treatment policies
by: Gilbert, Brian, et al.
Published: (2024)

Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition
by: Ross, Rachael K., et al.
Published: (2025)

Structural Nested Mean Models Under Parallel Trends with Interference
by: Shahn, Zach, et al.
Published: (2024)

Nonparametric estimation of an optimal treatment rule with fused randomized trials and missing effect modifiers
by: Williams, Nicholas, et al.
Published: (2025)

Double machine learning to estimate the effects of multiple treatments and their interactions
by: Xiang, Qingyan, et al.
Published: (2025)

Leveraging machine learning to estimate individualized treatment effects in cluster-randomized trials
by: Li, Changjun, et al.
Published: (2026)

Clarifying identification and estimation of treatment effects in the Sequential Parallel Comparison Design
by: Stockton, Benjamin, et al.
Published: (2025)

Causal survival analysis under competing risks using longitudinal modified treatment policies
by: Díaz, Iván, et al.
Published: (2022)

Average partial effect estimation using double machine learning
by: Klyne, Harvey, et al.
Published: (2023)

Studying continuous, time-varying, and/or complex exposures using longitudinal modified treatment policies
by: Hoffman, Katherine L., et al.
Published: (2023)

Improving efficiency in transporting average treatment effects
by: Rudolph, Kara E., et al.
Published: (2023)

Efficient estimation of subgroup treatment effects using multi-source data
by: Wang, Guanbo, et al.
Published: (2024)

Longitudinal weighted and trimmed treatment effects with flip interventions
by: McClean, Alec, et al.
Published: (2025)

Efficient nonparametric estimation with difference-in-differences in the presence of network dependence and interference
by: Jetsupphasuk, Michael, et al.
Published: (2025)

Estimating treatment effects with a unified semi-parametric difference-in-differences approach
by: Thome, Julia C., et al.
Published: (2025)

Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations
by: McClean, Alec, et al.
Published: (2025)

Orthogonal machine learning for conditional odds and risk ratios
by: Ge, Jiacheng, et al.
Published: (2026)

Efficient collaborative learning of the average treatment effect
by: Li, Sijia, et al.
Published: (2024)

A simple distributional difference-in-differences estimator for univariate and bivariate outcomes
by: Fernández-Val, Iván, et al.
Published: (2024)

Modified treatment policies that depend on the natural history of treatment
by: Díaz, Iván, et al.
Published: (2026)

Investigating the causal effects of multiple treatments using longitudinal data: a simulation study
by: Granger, Emily, et al.
Published: (2024)

Comment on "Generic machine learning inference on heterogeneous treatment effects in randomized experiments."
by: Imai, Kosuke, et al.
Published: (2025)

Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
by: Williams, Nicholas, et al.
Published: (2026)

Efficient semiparametric estimation of marginal treatment effects with genetic instrumental variables
by: Patel, Ashish, et al.
Published: (2026)

Efficient estimation of average treatment effects with unmeasured confounding and proxies
by: Ai, Chunrong, et al.
Published: (2025)

Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments"
by: Chernozhukov, Victor, et al.
Published: (2025)

Efficient estimation of the target population average treatment effect from multi-source data
by: Su, Zehao, et al.
Published: (2024)

Repeated sampling of different individuals but the same clusters to improve precision of difference-in-differences estimators: the DISC design
by: Downey, Jordan, et al.
Published: (2024)

Simulations for estimation of random effects and overall effect in three-level meta-analysis of standardized mean differences using constant and inverse-variance weights
by: Kulinskaya, Elena, et al.
Published: (2024)

Trustworthy assessment of heterogeneous treatment effect estimator
by: Gao, Zijun
Published: (2024)

Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death
by: McGrath, Sean, et al.
Published: (2025)

Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments
by: Martin, Axel, et al.
Published: (2024)

Robust evaluation of treatment effects in longitudinal studies with truncation by death or other intercurrent events
by: Baklicharov, Georgi, et al.
Published: (2025)

Identification and estimation of causal effects using non-concurrent controls in platform trials
by: Santacatterina, Michele, et al.
Published: (2024)

On the estimation of complex statistics combining different surveys
by: Chasiotis, Vasilis, et al.
Published: (2024)

Successive classification learning for estimating quantile optimal treatment regimes
by: Xia, Junwen, et al.
Published: (2025)