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Causal Inference 5 – Conclusions
The Marginal Structural Model (MSM) is used for assessing the causal effects on treatments when there exist time-varying confounders. These confounders are also affected and affect previous or future assignments. Because of the complicated structure and time-varying properties, we cannot use standard statistical methods. In the previous web pages, we show the applications and rationales…
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Causal Inference 4 – Sensitivity Analysis
Introduction and Notations This page introduces two methods for assessing sensitivity to unmeasured confounding in marginal structural model (Matteo, Edward, Valérie, and Larry, 2022). Recall that we have 3 usual assumptions in MSM: consistency (No interference), Positivity (Overlap), and Sequential Exchangeability (No unmeasured confounding). In the previous discussions, we knew the treatment is as good…
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Causal Inference 3 – Estimation
IPTW and IPCW The Marginal Structural Model (MSM) is a Cox-typed model, which is adjusted selection bias by inverse probability treatment weighting (IPTW) and inverse probability censoring weighting (IPCW). We introduce these two tools in this section. Using the same notations (see Causal Inference 1 ), we can write the propensity score as . Then,…
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Causal Inference 2 – Identification
Set up: simplification Before we discuss our identification, we need to simplify our scenario as the following DAG : To interpret identification easily, we simplify our scenario at first. Next, we introduce the assumptions. Assumptions We need the following set of assumptions to identify causal effect: We need sequential exchangeability rather than conditional exchangeability, because…
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Causal Inference 1 – Introduction
Interest Questions A time-dependent confounder that is affected by previous treatment or confounders is a complicated problem in survival analysis. Because time-dependent Cox model may produce biased effect estimates under the exsistence of such confounders, we cannot use Cox model but need another method to solve the problem. For example, we want to estimate the…
Po-Kuei Chen 