Category: Causal Inferance

NEED In the pharmaceutical industry, the demand for deeper insights into treatment effectiveness and patient outcomes has driven a significant interest in causal inference, particularly using real-world data (RWD). Unlike clinical trials, which are often limited by controlled environments and

NEED Uplift modeling is a technique used in predictive analytics to estimate the impact of a treatment or intervention on an outcome. It involves predicting the individual treatment effect (ITE) for each individual in the dataset, and then using these

What is Uplift Models Uplift modeling, also known as incremental modeling or true lift modeling, is a machine learning technique used to estimate the individual treatment effect of an intervention, such as a marketing campaign or medical treatment, on a

Here are the general steps for implementing propensity score matching to estimate treatment effects in an observational study: Define the research question: Start by defining the research question you want to answer. What is the treatment you want to evaluate?

NEED Propensity score matching is a statistical technique commonly used in observational studies to estimate treatment effects. In an observational study, researchers cannot control which patients receive treatment and which do not. Therefore, they must use statistical methods to adjust

Causal effect refers to the relationship between two variables, where one variable (the cause) influences the other variable (the effect). The causal effect measures the impact of the cause on the effect, controlling for other possible factors that may influence

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