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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
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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
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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
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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
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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
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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.
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1)Spurious Correlations: A statistical correlation with no causal implication. This is basically when a statistical correlation doesn’t imply causality. Here is an example – There is a huge correlation between the suicide rate and the amount of expenditure. But there
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Objective This module is meant to continuously track key measures captured in the baseline on a periodic basis. It allows one to keep tab on changes on a real time basis that allows for immediate action. Also can be seen
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Objective The largescale baseline exercise is conducted to gain an In-depth understanding of ‘party perception’ – Perceptual strength & weakness of a party vis-à-vis others In-depth understanding of ‘leader perception’ – Perceptual strength & weakness of a leader vis-à-vis others
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Objective A qualitative study is done with the objective of understanding The parameters that voter thinks are important and that the party should deliver The perception of voters on the delivery of promises made by central & state govt. How
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