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This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using…
Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the…
In this paper, we extend the recent body of work on planning under uncertainty to include the fact that sensors may not provide any measurement owing to misdetection. This is caused either by adverse environmental conditions that prevent…
Randomized saturation designs are a family of designs which assign a possibly different treatment proportion to each cluster of a population at random. As a result, they generalize the well-known (stratified) completely randomized designs…
Randomized clinical trials are the gold standard when estimating the average treatment effect. However, they are usually not a random sample from the real-world population because of the inclusion/exclusion rules. Meanwhile, observational…
For learning about the causal effect of a treatment, a randomized controlled trial (RCT) is considered the gold standard. However, randomizing treatment is sometimes unethical or infeasible, and instead an observational study may be…
Background: In settings where proof-of-principle trials have succeeded but the effectiveness of different forms of implementation remains uncertain, trials that not only generate information about intervention effects but also provide…
Benchmarking is crucial for testing and validating any system, even more so in real-time systems. Typical real-time applications adhere to well-understood abstractions: they exhibit a periodic behavior, operate on a well-defined working…
Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure learning can produce confident but misleading causal graphs…
Background: Target trial emulation (TTE) that applies trial design principles to improve the analysis of non-randomized studies is increasingly being used. Applications of TTE to emulate cluster randomized trials (RCTs) have been limited.…
We developed a study design for rare disease clinical trials (RDTs) that efficiently evaluate treatments, promotes access to new treatments during treatment development, and optimizes healthcare resource utilization for future treatment…
The gold standard for identifying causal relationships is a randomized controlled experiment. In many applications in the social sciences and medicine, the researcher does not control the assignment mechanism and instead may rely upon…
Randomized experiments are widely used to estimate causal effects across a variety of domains. However, classical causal inference approaches rely on critical independence assumptions that are violated by network interference, when the…
The use of information from real world to assess the effectiveness of medical products is becoming increasingly popular and more acceptable by regulatory agencies. According to a strategic real-world evidence framework published by U.S.…
Randomized benchmarking (RB) is a widely used strategy to assess the quality of available quantum gates in a computational context. RB involves applying known random sequences of gates to an initial state and using the statistics of a final…
In randomized controlled trials (RCTs), treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes which treat all units with probability one half, a certain matched-pair design achieves…
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A major…
This paper studies the evaluation of methods for targeting the allocation of limited resources to a high-risk subpopulation. We consider a randomized controlled trial to measure the difference in efficiency between two targeting methods and…
We develop randomization-based tests for heterogeneous treatment effects in the presence of network interference. Leveraging the exposure mapping framework, we study a broad class of null hypotheses that represent various forms of constant…
Randomized controlled trials typically assume that prognostic covariates are known and available at no cost. In practice, obtaining high-dimensional pretreatment data is costly, forcing a trade-off between covariate-adaptive precision and a…