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Causal Modeling

MTPPI helps healthcare organizations establish their own Causal Inference Center of Excellence (COEs). Causal Inference (CI) methods are critical tools for assessing the effectiveness of healthcare treatment strategies and interventions. CI methods allow healthcare workers to solve real-world problems using existing observational data. While standard statistics focus on finding correlations, causal methods use temporal precedence, the controlling of confounders, and counterfactual reasoning  to establish relationships between treatments and effects.


parametric g-formula

The parametric g-formula is a statistical method to estimate the causal effects of sustained treatment strategies from observational data with time-varying treatments, confounders, and outcomes. MTPPI can facilitate the application of the parametric g-formula to complex, real-world data to answer causal research questions. 


marginal structural models

Marginal structural models can be used to estimate the causal effect of a time-dependent exposure in the presence of time-dependent covariates. Marginal structural models handle the issue of time-dependent confounding by using inverse probability weighting for receipt of treatment which allows for the estimation of the average causal effects. MTPPI can help you build and operate marginal structural models for your observational research projects.  


instrumental variables

instrumental variables (IV) is a method used to estimate causal relationships when RCTs are not feasible or when a treatment is not successfully delivered to every patient in a randomized trial. MTPPI can help you deploy IV when  explanatory variables are correlated with the error terms in a regression model. Instrumental variables do not belong in the explanatory equation but are correlated with the endogenous explanatory variables conditionally on the value of other covariate.

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