• Defining and Estimating Causal Effects🔍
• Estimating causal effects🔍
• Frameworks for estimating causal effects in observational settings🔍
• Causal inference and effect estimation using observational data🔍
• Estimating causal effects from epidemiological data🔍
• From Controlled to Undisciplined Data🔍
• Estimating causal effects from large data sets using propensity scores🔍
• Estimating causal effects with optimization|based methods🔍

Defining and Estimating Causal Effects - NCBI

The causal effect of the experimental treatment (relative to the control) is defined by a comparison between how that child would fare under E versus C.

Estimating causal effects - Harvard University

',. 'How should we measure effects?' and 'What effect measure should epidemiologists estimate in etiologic studies?' We begin by adapting the counterfactual ...

Frameworks for estimating causal effects in observational settings

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to ...

Causal inference and effect estimation using observational data

We provide a clear, structured overview of key concepts and terms, intended as a starting point for readers unfamiliar with the causal inference literature.

Estimating causal effects | International Journal of Epidemiology

We describe how the counterfactual theory of causation, originally developed in philosophy and statistics, can be adapted to epidemiological studies.

Estimating causal effects from epidemiological data

This article reviews a condition that permits the estimation of causal effects from observational data, and two methods—standardisation and inverse probability ...

From Controlled to Undisciplined Data: Estimating Causal Effects in ...

Since machine learning methods aim to estimate the conditional mean of an outcome Y Y Y given X X X it would on the surface appear to be a good ...

Estimating causal effects from large data sets using propensity scores

The aim of many analyses of large databases is to draw causal inferences about the effects of actions, treatments, or interventions.

Estimating causal effects with optimization-based methods: A review ...

In this review paper, we present an overview of the causal inference literature and describe in more detail the optimization-based causal inference methods.

Defining, identifying, and estimating causal effects with the potential ...

Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and ...

Estimating Causal Effects: Using Experimental and Observational ...