Optimal Recovery for Causal Inference

It is crucial to successfully quantify causal effects of a policy intervention to determine whether the policy achieved the desired outcomes. We present a deterministic approach to a classical method of policy evaluation, synthetic control (Abadie and Gardeazabal, 2003), to estimate the unobservable outcome of a treatment unit using ellipsoidal optimal recovery (EOpR). EOpR provides policy evaluators with "worst-case" outcomes and "typical" outcomes to help in decision making. It is an approximation-theoretic technique that also relates to the theory of principal components, which recovers unknown observations given a learned signal class and a set of known observations. We show that EOpR can improve pre-treatment fit and bias of the post-treatment estimation relative to other econometrics methods. Beyond recovery of the unit of interest, an advantage of EOpR is that it produces worst-case estimates over the estimations produced by the recovery. We assess our approach on artificially-generated data, on datasets commonly used in the econometrics literature, and also derive results in the context of the COVID-19 pandemic. Such an approach is novel in the econometrics literature for causality and policy evaluation

Evidence-based health care in the occupied Palestinian territory: findings from a conference-based preparatory workshop

Background The principles of evidence-based health-care (EBHC) are not widely appreciated in the occupied Palestinian territory. During the past 5 years, interest in EBHC in Gaza has been generated through a series of lectures and workshops run by the EBHC Unit in Gaza. To further promulgate the principles of EBHC in the occupied Palestinian territory and to raise awareness of differences between local practice and best evidence, a 2 day conference was organised in October 2013. In this study our objectives were to promote the principles of EBHC and to improve clinical practice in 15 specific areas of clinical practice. Methods Five subcommittees were established 6 months before the conference that addressed general surgery, medicine, paediatrics, obstetrics, and orthopaedics and neurosurgery. Each subcommittee comprised a senior and a junior specialist and was given 5 months to identify the three most

Learning Optimal Features via Partial Invariance

Learning models that are robust to distribution shifts is a key concern in the context of their real-life applicability. Invariant Risk Minimization (IRM) is a popular framework that aims to learn robust models from multiple environments. The success of IRM requires an important assumption: the underlying causal mechanisms/features remain invariant across environments. When not satisfied, we show that IRM can over-constrain the predictor and to remedy this, we propose a relaxation via $\textit{partial invariance}$. In this work, we theoretically highlight the sub-optimality of IRM and then demonstrate how learning from a partition of training domains can help improve invariant models. Several experiments, conducted both in linear settings as well as with deep neural networks on tasks over both language and image data, allow us to verify our conclusions.Comment: Presented at the 37th AAAI Conference on Artificial Intelligence, 202

Occurrence of Yersinia enterocolitica and Aeromonas hydrophila in Clinical, Food and Environmental Samples in Gaza Strip

The interest on the occurrence of Yersinia enterocolitica and Aeromonas hydrophila, their pathogenicity and antimicrobial resistance is increasing worldwide because both were linked to acute and chronic gastroenteritis, septicemia and wound infections. Though reports on the occurrence of both pathogens among human that are available from certain areas, no published data are available from Gaza strip. Moreover, there are no routine methods for the detection of Yersinia and Aeromonas in clinical or environmental samples. Hence this study investigated the occurrence of both Y. enterocolitica and A. hydrophila in clinical and environmental samples. Of the 473 clinical and environmental samples, 28 (5.9%) were positive for Y. enterocolitica and 179 (38.1%) for A. hydrophila. With high incidence of Y. enterocolitica and A. hydrophila in sewage (19.1%) and water (46.9%) respectively. The overall incidence of Y. enterocolitica and A. hydrophila in clinical samples was 4.7% and 34.3% respectively, with high frequency of both pathogens in AL-Dorrah and AL-Nasser hospitals. Virulence of isolates was assessed and their antimicrobial resistance to 20 antimicrobial agents was evaluated. Both clinical and environmental isolates possessed virulence factors with higher frequency in clinical samples. Antibiotic susceptibility testing revealed that most of Y. enterocolitica isolates were sensitive to most antibiotics; on the other hand, most of A. hydrophila isolates showed multiple antibiotic resistances. The most effective antimicrobials on A. hydrophila were azetreonam, ciprofloxacin and ofloxacin

Designing Discontinuities

Discontinuities can be fairly arbitrary but also cause a significant impact on outcomes in social systems. Indeed, their arbitrariness is why they have been used to infer causal relationships among variables in numerous settings. Regression discontinuity from econometrics assumes the existence of a discontinuous variable that splits the population into distinct partitions to estimate the causal effects of a given phenomenon. Here we consider the design of partitions for a given discontinuous variable to optimize a certain effect previously studied using regression discontinuity. To do so, we propose a quantization-theoretic approach to optimize the effect of interest, first learning the causal effect size of a given discontinuous variable and then applying dynamic programming for optimal quantization design of discontinuities that balance the gain and loss in the effect size. We also develop a computationally-efficient reinforcement learning algorithm for the dynamic programming formulation of optimal quantization. We demonstrate our approach by designing optimal time zone borders for counterfactuals of social capital, social mobility, and health. This is based on regression discontinuity analyses we perform on novel data, which may be of independent empirical interest in showing a causal relationship between sunset time and social capital.Comment: A short version is accepted in Neural Compression ICML Worksop July 19th, 202

Antibiogram of bacterial isolates from clinical specimens during 2018-2020 at Al-Aqsa hospital, Gaza, Palestine

Background: The increased resistance of microorganisms to widely prescribed antibiotics in current medical practice has become a major challenge. Healthcare-associated infections (HAIs) are complications of healthcare and linked with high morbidity and mortality. This study aims to investigate the susceptibility pattern of bacteria isolated from different bacterial infections to commonly used antimicrobials from Al-Aqsa hospital in Gaza Strip, Palestine. Methods: A total of 8062 various clinical specimens were collected from August 2018 to February 2020 and sent to Al-Aqsa medical microbiology laboratory for bacteriological culture. Specimens were processed based on the recommended microbiology procedures. The modified Kirby-Bauer disc diffusion method was used for antimicrobial susceptibility testing on Muller Hinton agar (MHA) as per the Clinical Laboratory Standards Institute (CLSI) guideline. Results: Enterobacteriaceae were the most frequent of all isolated pathogens (58.3%), followed by Staphylococcus spp (24.6%), Pseudomonas and Non-fermenters (6.9%), Streptococcus and Enterococcus (6.2%), and others (4.0). E. coli was the most frequent of all isolated pathogens (38.2%), followed by Coagulase Negative Staphylococci (14.9%), Klebsiella spp (14.2%), and Staphylococcus aureus (9.4%). The resistance of Gram-negative isolates for Piperacillin, Cephalexin, Cefuroxime, Cefotaxim, Ceftazidim, Ceftriaxone, Cefazolin, Co Trimoxazole, Nalidixic acid, Aztreonam, Amoxicillin/clavulanic acid, Meropenem and Techoplanin was between 62% and 92%. On the other hand, Gram-positive isolates (Staphylococcus spp) were found susceptible to Cloxacillin (65.0%), Erythromycin (47.3%), Clindamycin (81.7%), Levofloxacin (100.0%), Rifampicin (95.2%) and Vancomycin (89.2%). Conclusion: High rates of resistance were found among bacterial pathogens isolated from Al-Aqsa hospital. Regular antimicrobial resistance surveillance should be a continuous process to provide up-to-date information to physicians with local antimicrobial resistance data. Keywords: Antimicrobial resistance, Healthcare-associated infections, Gaza, Palestine.

Periodically Correlated Time Series Models: Representation and Identification

This thesis is interested with some characteristics of a class periodically correlated (PC) time series models. We study both periodically correlated time series models and multiple models and discuss the relationship between them. Give many examples. We discuss in this work many representations of PC models and use these representations in order make evidence that PC class and multiple AR models are theoretically the same. In addition, we propose a new representation, the multi-companion (MC) representation. Give an example. We discuss the relationship between the vector moving average and the periodic moving average, and give an examples to clarify the relationship between them. Also, we study the periodic autoregressive moving-average (PARMA) models and their representations. We show that any PARMA model can be expressed as a vector ARMA model. We consider the identification of orders of periodic AR (PAR) models by extending well known techniques to periodic time series, we discuss methods for specifying models and for efficiently estimating the parameters in those models. Finally, a detailed simulation study is given to illustrate the procedures

Integration of Evidence Based Medicine into a Medical Curriculum

The College of Medicine at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) was established in January 2004. The four-year curriculum was based on the Problem Based Learning (PBL) format and involved the web-based graduate medical program adopted from the University of Sydney, Australia. At KSAU-HS, one additional semester was added to the beginning of this curriculum to prepare the students in English language skills, PBL, Information Technology and Evidence Based Medicine (EBM). EBM is part of the Personal and Professional Development (PPD) theme of the medical curriculum and is integrated into each stage of the medical curriculum. These modifications of the University of Sydney curriculum are presented here as a model of EBM integration into a college of medicine curriculum

The ongoing pursuit of neuroprotective therapies in Parkinson disease

Many agents developed for neuroprotective treatment of Parkinson disease (PD) have shown great promise in the laboratory, but none have translated to positive results in patients with PD. Potential neuroprotective drugs, such as ubiquinone, creatine and PYM50028, have failed to show any clinical benefits in recent high-profile clinical trials. This 'failure to translate' is likely to be related primarily to our incomplete understanding of the pathogenic mechanisms underlying PD, and excessive reliance on data from toxin-based animal models to judge which agents should be selected for clinical trials. Restricted resources inevitably mean that difficult compromises must be made in terms of trial design, and reliable estimation of efficacy is further hampered by the absence of validated biomarkers of disease progression. Drug development in PD dementia has been mostly unsuccessful; however, emerging biochemical, genetic and pathological evidence suggests a link between tau and amyloid-β deposition and cognitive decline in PD, potentially opening up new possibilities for therapeutic intervention. This Review discusses the most important 'druggable' disease mechanisms in PD, as well as the most-promising drugs that are being evaluated for their potential efficiency in treatment of motor and cognitive impairments in PD