What do we mean with “Interpretable ML”?
There are several reasons to make a “black box” machine learning model interpretable. First, an interpretable output can be more readily understood and trusted by its users (for example, clinicians deciding whether to prescribe a treatment), making its outputs more actionable. Second, a model’s outputs often need to be explained by its users to the subjects of its outputs (for example, patients deciding whether to accept a proposed treatment course) . Third, by uncovering valuable information that otherwise would have remained hidden within the model’s opaque inner workings, an interpretable output can empower users such as researchers with powerful new insights.
The value of interpretability as a broad concept is, therefore, clear. Yet despite this, the meaning of the term itself is too seldom discussed and too often oversimplified. There is no single “type” of interpretability, after all, since there are many potential ways to extract and present information from the output of a model, and many types of information to choose to extract.
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Our Research
Our framework divides interpretability into 4 broad “types”:
1) feature importance;
2) similarity classification;
3) transparent mathematical equations
4) concept-based explanations
Machine Learning for Pharmacology
There are close links between our work on treatment effect estimation and pharmacology. In addition, our belief at the van der Schaar lab is that the integration of machine learning with pharmacology will unlock new frontiers in personalised medicine and clinical trials.
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To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
Estimating Heterogeneous Treatment Effects
To aid healthcare professionals in making treatment decisions, we need methods that can accurately forecast the effects of different treatments for individual patients.
However, most machine learning (ML) models are not designed for this. Traditional ML models make outcome predictions that are exactly in line with the distribution of the training data, but they struggle to reason about outcomes under interventions – key to understanding the effects of treatments. Unlike standard prediction, treatment effect estimation involves predicting what would happen under alternative treatment scenarios, making it a far more complex task.
Our lab develops ML algorithms specifically designed for the estimation of conditional average treatment effects (CATE), advancing these methods toward clinical use and addressing the unique challenges they present.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
Treatment Effect Estimation Using Time-Series Data
While work on static treatment effects can already provide valuable insights, real-world data often involves more complex, time-dependent scenarios, such as when drug efficacy changes over time or patients receive multiple treatments at different stages.
Estimating treatment effects in these dynamic contexts allows us to understand how diseases evolve under different treatment strategies and to identify optimal intervention timings. Traditional static methods for treatment effect estimation struggle to capture these complexities, highlighting the need for approaches designed for time-varying treatments, which are able to take advantage of time-series data.
Our lab is at the forefront of designing such ML methods, advancing treatment effect estimation for more dynamic, real-world decision-making.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
Extending the Applicability of Treatment Effect Estimation
Extending the applicability of treatment effect estimation methods requires addressing additional complexities that arise in real-world settings. As healthcare decisions become increasingly nuanced, it is essential to adapt these methods to account for various challenges that may impact their effectiveness. For instance, we focus on several critical areas, including time-to-event data, where outcomes unfold over time and may be influenced by competing events.
Our work also addresses policy analysis for evaluating healthcare interventions, the identification of patient subgroups in clinical trials that benefit most from treatments, and the development of generalised causal sensitivity analysis techniques to assess the robustness of estimates against assumptions and unmeasured confounding. These advancements broaden the scope of treatment effect estimation methods for diverse clinical and policy applications.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
Real-World Applications of Treatment Effect Estimation
The real-world applications of treatment effect estimation methods demonstrate their potential to significantly impact clinical practice and research. However, to fulfil their potential, these methods need to be well understood and adopted by the clinical community.
We aim to bridge the gap between research and practice, empowering healthcare professionals with tools that enhance their understanding of treatment effects and support evidence-based medicine. Through these applications, we contribute to shaping the future of medical practice and advancing personalised healthcare.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
ICLR 2023
Missing data in real-world scenarios complicates the accurate estimation of treatment effects, as traditional assumptions don’t account for the interaction between treatment variables and missing data. The authors introduce a new mechanism, mixed confounded missingness (MCM), showing that standard imputation methods can erase key information, while ignoring imputation leads to bias. They propose selective imputation, which determines which variables to impute, and demonstrate its effectiveness in improving treatment effect models across various algorithms.
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