Add to ORKGThe recognition that personalised treatment decisions lead to better clinical outcomes has sparked recent research activity in the following two domains. Policy learning focuses on finding optimal treatment rules (OTRs), which express whether an individual would be better off with or without treatment, given their measured characteristics. OTRs optimize a pre-set population criterion, but do not provide insight into the extent to which treatment benefits or harms individual subjects. Estimates of conditional average treatment effects (CATEs) do offer such insights, but valid inference is currently difficult to obtain when data-adaptive methods are used. Moreover, clinicians are (rightly) hesitant to blindly adopt OTR or CATE estimates, not ...
Causal inference from observational data requires untestable identification assumptions. If these as...
Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into m...
Consider some experimental treatment, such as taking a drug or attending a job training pro-gram. It...
Estimating heterogeneous treatment effects is a well-studied topic in the statistics literature. Mor...
With the rise of large and fine-grained data sets, there is a desire for researchers, physicians, bu...
There is an increasing appeal in answering causal questions, and this interest has drawn perspective...
This dissertation focuses on modern causal inference under uncertainty and data restrictions, with a...
Recent advances in causal machine learning leverage observational data to estimate heterogeneous tre...
We propose a doubly robust approach to characterizing treatment effect heterogeneity in observationa...
Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X...
Randomized control trials are sometimes used to estimate the aggregate benefit from some policy or p...
Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide va...
This article examines a causal machine-learning approach, causal forests (CF), for exploring the het...
Large observational data are increasingly available in disciplines such as health, economic and soci...
In the study of causal inference, statisticians show growing interest in estimating and analyzing he...
Causal inference from observational data requires untestable identification assumptions. If these as...
Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into m...
Consider some experimental treatment, such as taking a drug or attending a job training pro-gram. It...
Estimating heterogeneous treatment effects is a well-studied topic in the statistics literature. Mor...
With the rise of large and fine-grained data sets, there is a desire for researchers, physicians, bu...
There is an increasing appeal in answering causal questions, and this interest has drawn perspective...
This dissertation focuses on modern causal inference under uncertainty and data restrictions, with a...
Recent advances in causal machine learning leverage observational data to estimate heterogeneous tre...
We propose a doubly robust approach to characterizing treatment effect heterogeneity in observationa...
Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X...
Randomized control trials are sometimes used to estimate the aggregate benefit from some policy or p...
Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide va...
This article examines a causal machine-learning approach, causal forests (CF), for exploring the het...
Large observational data are increasingly available in disciplines such as health, economic and soci...
In the study of causal inference, statisticians show growing interest in estimating and analyzing he...
Causal inference from observational data requires untestable identification assumptions. If these as...
Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into m...
Consider some experimental treatment, such as taking a drug or attending a job training pro-gram. It...