nikolaospapachristou/causal-inference-in-epidemiology

Causal Inference in Epidemiology

Causal Inference with TMLE in Epidemiology: a little overview

During the last 30 years, Modern Epidemiology has been able to identify significant limitations of classic epidemiologic methods when the focus is to explain the main effect of a risk factor on a disease or outcome:

1. non-collapsibility of the odds and hazard ratios,
2. impact of paradoxical effects due to conditioning on colliders,
3. selection bias related to the vague understanding of the effect of time on exposure and outcome and,
4. effect of time-dependent confounding and mediators,
5. etc.

Classical epidemiologic methods to control for confounding require making the assumption that the effect measure is constant across levels of confounders included in the model. Alternatively, James Robins in 1986 showed that using standardisation implemented through the use of the G-formula allows obtaining a unconfounded marginal estimation of the causal average treatment effect (ATE) under causal assumptions.

The most commonly used estimator for a binary treatment effect is the risk difference or ATE. The ATE estimation relies on parametric modelling assumptions. Therefore, the correct model specification is crucial to obtain unbiased estimates of the true ATE.

However, Mark van der Laan and collaborators have developed a double-robust estimation procedure to reduce bias against misspecification. The targeted maximum likelihood estimation (TMLE) is a semiparametric, efficient substitution estimator. TMLE allows for data-adaptive estimation while obtaining valid statistical inference based on the targeted minimum loss-based estimation and machine learning algorithms to minimise the risk of model misspecification.

Evidence shows that TMLE provides the less unbiased estimate of the ATE compared with other double robust estimators.

The R notebook (TMLE.nb.html) will guide you step by step through the implementation of the Targeted Maximum Likelihood Estimation (TMLE).

Please, download and open it in your favourite browser or visit the following link:
http://migariane.github.io/TMLE.nb.html
Hope you will enjoy it.

Best regards,
Miguel Angel Luque-Fernandez