Background: The fundamental problem of causal inference is one of missing data, and specifically of missing potential outcomes: if potential outcomes were fully observed, then causal inference could be made trivially. Though often not discussed explicitly in the epidemiological literature, the connections between causal inference and missing data can provide additional intuition
The age old quest for the golden grail of causal answers has been at the heart of science for centur...
Missing data is a problem that many researchers face, particularly when using large surveys. Informa...
We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (...
Background: The fundamental problem of causal inference is one of missing data, and specifically of ...
Epidemiologists often use the potential outcomes framework to cast causal inference as a missing dat...
From Springer Nature via Jisc Publications RouterHistory: received 2019-11-18, accepted 2020-06-28, ...
Epidemiologic studies are frequently susceptible to missing information. Omitting observations with ...
Causal interpretation of relationships is complicated by the ‘fundamental problem of causal inferenc...
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valua...
A practical guide to analysing partially observed data. Collecting, analysing and drawing inference...
Principled methods with which to appropriately analyze missing data have long existed; however, broa...
Estimating causal effects from incomplete data requires additional and inherently untestable assumpt...
With incomplete data, the missing at random (MAR) assumption is widely understood to enable unbiased...
Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the ...
In many observational studies, analysts estimate causal effects using propensity score matching. Est...
The age old quest for the golden grail of causal answers has been at the heart of science for centur...
Missing data is a problem that many researchers face, particularly when using large surveys. Informa...
We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (...
Background: The fundamental problem of causal inference is one of missing data, and specifically of ...
Epidemiologists often use the potential outcomes framework to cast causal inference as a missing dat...
From Springer Nature via Jisc Publications RouterHistory: received 2019-11-18, accepted 2020-06-28, ...
Epidemiologic studies are frequently susceptible to missing information. Omitting observations with ...
Causal interpretation of relationships is complicated by the ‘fundamental problem of causal inferenc...
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valua...
A practical guide to analysing partially observed data. Collecting, analysing and drawing inference...
Principled methods with which to appropriately analyze missing data have long existed; however, broa...
Estimating causal effects from incomplete data requires additional and inherently untestable assumpt...
With incomplete data, the missing at random (MAR) assumption is widely understood to enable unbiased...
Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the ...
In many observational studies, analysts estimate causal effects using propensity score matching. Est...
The age old quest for the golden grail of causal answers has been at the heart of science for centur...
Missing data is a problem that many researchers face, particularly when using large surveys. Informa...
We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (...