most citedMachine learning in policy evaluation: new tools for causal inference

11 citations · 11 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ML201911 cited

Machine learning in policy evaluation: new tools for causal inference

Noemi Kreif, Karla DiazOrdaz

While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluation…

stat.AP2019

Non-compliance and missing data in health economic evaluation

Karla DiazOrdaz, Richard Grieve

Health economic evaluations face the issues of non-compliance and missing data. Here, non-compliance is defined as non-adherence to a specific treatment, and occurs within randomis…

stat.ME2018

Estimating cluster-level local average treatment effects in cluster randomised trials with non-adherence

Schadrac C. Agbla, Bianca De Stavola, Karla DiazOrdaz

Non-adherence to assigned treatment is a common issue in cluster randomised trials (CRTs). In these settings, the efficacy estimand may be also of interest. Many methodological con…

stat.ME2018

Local average treatment effects estimation via substantive model compatible multiple imputation

Karla DiazOrdaz, James Carpenter

Non-adherence to assigned treatment is common in randomised controlled trials (RCTs). Recently, there has been an increased interest in estimating causal effects of treatment recei…

stat.ME2018

Data-adaptive doubly robust instrumental variable methods for treatment effect heterogeneity

Karla DiazOrdaz, Rhian Daniel, Noemi Kreif

We consider the estimation of the average treatment effect in the treated as a function of baseline covariates, where there is a valid (conditional) instrument. We describe two dou…