Academy of Medical Sciences of I.R. Iran Archives of Iranian Medicine 1029-2977 21 4 2018 04 01 Causal Methods for Observational Research: A Primer 164 169 EN Amir Almasi-Hashiani Saharnaz Nedjat Mohammad Ali Mansournia Journal Article 2018 01 12 2018 03 04 The goal of many observational studies is to estimate the causal effect of an exposure on an outcome after adjustment for confounders, but there are still some serious errors in adjusting confounders in clinical journals. Standard regression modeling (e.g., ordinary logistic regression) fails to estimate the average effect of exposure in total population in the presence of interaction between exposure and covariates, and also cannot adjust for time-varying confounding appropriately. Moreover, stepwise algorithms of the selection of confounders based on P values may miss important confounders and lead to bias in effect estimates. Causal methods overcome these limitations. We illustrate three causal methods including inverse-probability-of-treatment-weighting (IPTW) and parametric g-formula, with an emphasis on a clever combination of these 2 methods: targeted maximum likelihood estimation (TMLE) which enjoys a double-robust property against bias.