# Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity

arXiv: https://arxiv.org/abs/2510.22202 · HTML: https://mlsanity.com/paper/2510.22202/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Graphical Models for Processing Missing Data](https://mlsanity.com/paper/1801.03583/) (2018)
- [Using simulation studies to evaluate statistical methods](https://mlsanity.com/paper/1712.03198/) (2017)
- [Multiple Imputation: A Review of Practical and Theoretical Findings](https://mlsanity.com/paper/1801.04058/) (2018)
- [Efficient Estimation of Pathwise Differentiable Target Parameters with the Undersmoothed Highly Adaptive Lasso](https://mlsanity.com/paper/1908.05607/) (2019)
- [Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials](https://mlsanity.com/paper/2109.14048/) (2021)
- [Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review](https://mlsanity.com/paper/2303.07329/) (2023)
- [The `Why' behind including `Y' in your imputation model](https://mlsanity.com/paper/2310.17434/) (2023)
- [Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation](https://mlsanity.com/paper/2112.05274/) (2021)
- [Recoverability and estimation of causal effects under typical multivariable missingness mechanisms](https://mlsanity.com/paper/2301.06739/) (2023)
- [Recoverability of Causal Effects under Presence of Missing Data: a Longitudinal Case Study](https://mlsanity.com/paper/2402.14562/) (2024)

## Abstract (arXiv)

We evaluate the performance of targeted maximum likelihood estimation (TMLE) for estimating the average treatment effect in missing data scenarios under varying levels of positivity violations. We employ model- and design-based simulations, with the latter using undersmoothed highly adaptive lasso on the 'WASH Benefits Bangladesh' dataset to mimic real-world complexities. Five missingness-directed acyclic graphs are considered, capturing common missing data mechanisms in epidemiological research, particularly in one-point exposure studies. These mechanisms include also not-at-random missingness in the exposure, outcome, and confounders. We compare eight missing data methods in conjunction with TMLE as the analysis method, distinguishing between non-multiple imputation (non-MI) and multiple imputation (MI) approaches. The MI approaches use both parametric and machine-learning models. Results show that non-MI methods, particularly complete cases with TMLE incorporating an outcome-missingness model, exhibit lower bias compared to all other evaluated missing data methods and greater robustness against positivity violations across. In Comparison MI with classification and regression trees (CART) achieve lower root mean squared error, while often maintaining nominal coverage rates. Our findings highlight the trade-offs between bias and coverage, and we recommend using complete cases with TMLE incorporating an outcome-missingness model for bias reduction and MI CART when accurate confidence intervals are the priority.