Dr Venugopala Rao Manneni

There are different types of causal effects, and the choice of which type to focus on may depend on the research question, study design, and available data. Some common types of causal effects include: Individual Treatment Effect (ITE): The effect

Causality is an essential concept in data science because it helps us understand the underlying mechanisms that drive relationships between variables, which is critical for making accurate predictions and designing effective interventions. In many data science applications, the goal is

NEED Bias and confounding are common sources of error in observational studies that can undermine the validity of the results. Here are some strategies to handle bias and confounding in observational studies: Bias: a) Selection Bias: Selection bias can be

In observational studies, bias and confounding are important sources of error that can affect the validity of the results. Here are some ways to identify bias and confounding in an observational study: Bias: Bias occurs when the results of a

Causal inference is important in healthcare observational studies because it allows researchers to determine whether a particular treatment or intervention has a causal effect on health outcomes. In healthcare, observational studies are often conducted when it is not possible or

Causal inference is important in healthcare because it allows researchers, healthcare providers, and policymakers to understand the causal relationships between different factors and health outcomes. This is critical for developing effective interventions, improving patient outcomes, and optimizing healthcare resource allocation.