Synthetic Interventions

Consider a setting where there are $N$ heterogeneous units (e.g., individuals, sub-populations) and $D$ interventions (e.g., socio-economic policies). Our goal is to learn the potential outcome associated with every intervention on every unit (i.e., $N \times D$ causal parameters). Towards this, we present a causal framework, synthetic interventions (SI), to infer these $N \times D$ causal parameters while only observing each of the $N$ units under at most two interventions, independent of $D$. This can be significant as the number of interventions, i.e, level of personalization, grows. Importantly, our estimator also allows for latent confounders that determine how interventions are assigned. Theoretically, under a novel tensor factor model across units, measurements, and interventions, we formally establish an identification result for each of these $N \times D$ causal parameters and establish finite-sample consistency and asymptotic normality of our estimator. The estimator is furnished with a data-driven test to verify its suitability. Empirically, we validate our framework through both experimental and observational case studies; namely, a large-scale A/B test performed on an e-commerce platform, and an evaluation of mobility restriction on morbidity outcomes due to COVID-19. We believe this has important implications for program evaluation and the design of data-efficient RCTs with heterogeneous units and multiple interventions

Synthetic Controls: A New Approach to Evaluating Interventions

Synthetic control methods are a novel approach to comparative case study research using observational data. Though developed within political science, the methods can potentially be applied to a wide range of evaluation problems in economics, public health, social policy and other disciplines. In the traditional approach, an area in which a new or redesigned service is being implemented is compared with another ‘control’ area (in which there is no change) and statistical adjustment used to account for any differences between areas that might bias the comparison. In the new approach, a synthetic control is derived using data on past trends in all potentially comparable areas, providing a more robust basis for identifying the impact of the service change. Synthetic control methods may be a valuable addition to the range of techniques available for non-randomised evaluations of social, economic and public health interventions. To date there have been few applications in a UK context, and none in Scotland. Published evidence suggests considerable potential to apply synthetic controls to public service innovations at NHS Board, local authority or Community Planning Partnership level, and may widen the range of policy and practice changes that can usefully be evaluated

Synthetic Mudscapes: Human Interventions in Deltaic Land Building

In order to defend infrastructure, economy, and settlement in Southeast Louisiana, we must construct new land to mitigate increasing risk. Links between urban environments and economic drivers have constrained the dynamic delta landscape for generations, now threatening to undermine the ecological fitness of the entire region. Static methods of measuring, controlling, and valuing land fail in an environment that is constantly in flux; change and indeterminacy are denied by traditional inhabitation. Multiple land building practices reintroduce deltaic fluctuation and strategic deposition of fertile material to form the foundations of a multi-layered defence strategy. Manufactured marshlands reduce exposure to storm surge further inland. Virtual monitoring and communication networks inform design decisions and land use becomes determined by its ecological health. Mudscapes at the threshold of land and water place new value on former wastelands. The social, economic, and ecological evolution of the region are defended by an expanded web of growing land

Causal Imputation via Synthetic Interventions

Consider the problem of determining the effect of a compound on a specific cell type. To answer this question, researchers traditionally need to run an experiment applying the drug of interest to that cell type. This approach is not scalable: given a large number of different actions (compounds) and a large number of different contexts (cell types), it is infeasible to run an experiment for every action-context pair. In such cases, one would ideally like to predict the outcome for every pair while only having to perform experiments on a small subset of pairs. This task, which we label "causal imputation", is a generalization of the causal transportability problem. To address this challenge, we extend the recently introduced synthetic interventions (SI) estimator to handle more general data sparsity patterns. We prove that, under a latent factor model, our estimator provides valid estimates for the causal imputation task. We motivate this model by establishing a connection to the linear structural causal model literature. Finally, we consider the prominent CMAP dataset in predicting the effects of compounds on gene expression across cell types. We find that our estimator outperforms standard baselines, thus confirming its utility in biological applications

Can older people remember medication reminders presented using synthetic speech?

Reminders are often part of interventions to help older people adhere to complicated medication regimes. Computer-generated (synthetic) speech is ideal for tailoring reminders to different medication regimes. Since synthetic speech may be less intelligible than human speech, in particular under difficult listening conditions, we assessed how well older people can recall synthetic speech reminders for medications. 44 participants aged 50-80 with no cognitive impairment recalled reminders for one or four medications after a short distraction. We varied background noise, speech quality, and message design. Reminders were presented using a human voice and two synthetic voices. Data were analyzed using generalized linear mixed models. Reminder recall was satisfactory if reminders were restricted to one familiar medication, regardless of the voice used. Repeating medication names supported recall of lists of medications. We conclude that spoken reminders should build on familiar information and be integrated with other adherence support measures. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: [email protected] numbered affiliations see end of article

Synthetic cannabinoid use in a case series of patients with psychosis presenting to acute psychiatric settings : Clinical presentation and management issues

Background: Novel Psychoactive Substances (NPS) are a heterogeneous class of synthetic molecules including synthetic cannabinoid receptor agonists (SCRAs). Psychosis is associated with SCRAs use. There is limited knowledge regarding the structured assessment and psychometric evaluation of clinical presentations, analytical toxicology and clinical management plans of patients presenting with psychosis and SCRAs misuse. Methods: We gathered information regarding the clinical presentations, toxicology and care plans of patients with psychosis and SCRAs misuse admitted to inpatients services. Clinical presentations were assessed using the PANSS scale. Vital signs data were collected using the National Early Warning Signs tool. Analytic chemistry data were collected using urine drug screening tests for traditional psychoactive substances and NPS. Results: We described the clinical presentation and management plan of four patients with psychosis and misuse of SCRAs. Conclusion: The formulation of an informed clinical management plan requires a structured assessment, identification of the index NPS, pharmacological interventions, increases in nursing observations, changes to leave status and monitoring of the vital signs. The objective from using these interventions is to maintain stable physical health whilst rapidly improving the altered mental state.Peer reviewedFinal Published versio

Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders

We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the observational data but also sets of single-variable interventions in which each of the treatment variables is intervened on separately. We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model with additive Gaussian noise. In addition, we propose a simple parameter estimation method by pooling all the data from different regimes and jointly maximizing the combined likelihood. We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.Comment: Accepted to The Conference on Uncertainty in Artificial Intelligence (UAI) 202

Synthetic Social Support: Theorizing Lay Health Worker Interventions

Levels of social support are strongly associated with health outcomes and inequalities. The use of lay health workers (LHWs) has been suggested by policy makers across the world as an intervention to identify risks to health and to promote health, particularly in disadvantaged communities. However, there have been few attempts to theorize the work undertaken by LHWs to understand how interventions work. In this article, the authors present the concept of 'synthetic socialsupport' and distinguish it from the work of health professionals or the spontaneous social support received from friends and family. The authors provide new empirical data to illustrate the concept based on qualitative, observational research, using a novel shadowing method involving clinical and non-clinical researchers, on the everyday work of 'pregnancy outreach workers' (POWs) in Birmingham, UK. The service was being evaluated as part of a randomized controlled trial. These LHWs provided instrumental, informational, emotional and appraisal support to the women they worked with, which are all key components of social support. The social support was 'synthetic' because it was distinct from the support embedded in spontaneous social networks: it was non-reciprocal; it was offered on a strictly time-limited basis; the LHWs were accountable for the relationship, and the social networks produced were targeted rather than spontaneous. The latter two qualities of this synthetic form of social support may have benefits over spontaneous networks by improving the opportunities for the cultivation of new relationships (both strong and weak ties) outside the women's existing spontaneous networks that can have a positive impact on them and by offering a reliable source of health information and support in a chaotic environment. The concept of SSS can help inform policy makers about how deploying lay workers may enable them to achieve desired outcomes, specify their programme theories and evaluate accordingly. [Abstract copyright: Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.