CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images

The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imagery into the study of cause and effect. One set of functions enables image-based causal inference analyses. For example, one key function decomposes treatment effect heterogeneity by images using an interpretable Bayesian framework. This allows for determining which types of images or image sequences are most responsive to interventions. A second modeling function allows researchers to control for confounding using images. The package also allows investigators to produce embeddings that serve as vector summaries of the image or video content. Finally, infrastructural functions are also provided, such as tools for writing large-scale image and image sequence data as sequentialized byte strings for more rapid image analysis. causalimages therefore opens new capabilities for causal inference in R, letting researchers use informative imagery in substantive analyses in a fast and accessible manner.Comment: For accompanying software, see https://github.com/AIandGlobalDevelopmentLab/causalimages-softwar

Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa

Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well understood. However, in public policy, ecology, and in medicine, decisions are often made in non-tabular settings, informed by patterns or objects detected in images (e.g., maps, satellite or tomography imagery). Using such imagery for causal inference presents an opportunity because objects in the image may be related to the treatment and outcome of interest. In these cases, we rely on the images to adjust for confounding but observed data do not directly label the existence of the important objects. Motivated by real-world applications, we formalize this challenge, how it can be handled, and what conditions are sufficient to identify and estimate causal effects. We analyze finite-sample performance using simulation experiments, estimating effects using a propensity adjustment algorithm that employs a machine learning model to estimate the image confounding. Our experiments also examine sensitivity to misspecification of the image pattern mechanism. Finally, we use our methodology to estimate the effects of policy interventions on poverty in African communities from satellite imagery

Serological evaluation of toxocarosis in Amedyia District, Duhok Governorate, Kurdistan Region Iraq

The ova of Toxocara canis is common environmental contaminants of human habitation, due to the fact that dogs serve as final hosts. The presence of Toxocara ova in the soil which considerable as a risky factor for human public health. Humans, particularly children, frequently ingest these ova accidently and infected with disease. Infection in humans, in contrast to their definitive hosts, remains unusual host, often resulting in disease caused by the migrating larval stages without development to adult stage. This study is the first study in Duhok Governorate and Kurdistan region to determine the seroprevalance of toxocarosis among human population and the relation of the associated factors. A total of 600 blood samples were collected from children and adults of different ages (5-70) years and both genders. Blood samples were collected in a gel tubes, then centrifuged for isolation of sera. The sera were kept at -20ºC until used for detection of anti Toxocara canis IgG antibodies using ELISA. Out of 600 serum samples 38 (6.3 %) were seropositive. The rate of infection was higher in females 22/285 (7.7%) than males 16/315 (5.1%). The individuals belong the age group less than 5 years were more prevalent (21.4%) than other groups followed by age group (5-14) year with infection rate (9.6%). Toxocarosis is prevalent in adults and children of Amedyia district. These results require periodical studies on the rate of infection and associated risk factors in other areas of Duhok Governorate and Kurdistan region

A Static Optimality Transformation with Applications to Planar Point Location

Over the last decade, there have been several data structures that, given a planar subdivision and a probability distribution over the plane, provide a way for answering point location queries that is fine-tuned for the distribution. All these methods suffer from the requirement that the query distribution must be known in advance. We present a new data structure for point location queries in planar triangulations. Our structure is asymptotically as fast as the optimal structures, but it requires no prior information about the queries. This is a 2D analogue of the jump from Knuth's optimum binary search trees (discovered in 1971) to the splay trees of Sleator and Tarjan in 1985. While the former need to know the query distribution, the latter are statically optimal. This means that we can adapt to the query sequence and achieve the same asymptotic performance as an optimum static structure, without needing any additional information.Comment: 13 pages, 1 figure, a preliminary version appeared at SoCG 201

Transport And Plugging Performance Evaluation Of A Novel Re-Crosslinkable Microgel Used For Conformance Control In Mature Oilfields With Super-Permeable Channels

Preformed particle gels (PPG) have been widely applied in oilfields to control excessive water production. However, PPG has limited success in treating opening features because the particles can be flushed readily during post-water flooding. We have developed a novel micro-sized Re-crosslinkable PPG (micro-RPPG) to solve the problem. The microgel can re-crosslink to form a bulk gel, avoiding being washed out easily. This paper evaluates the novel microgels\u27 transport and plugging performance through super-permeable channels. Micro-RPPG was synthesized and evaluated for this study. Its storage moduli after fully swelling are approximately 82 Pa. The microgel characterization, self-healing process, transportation behavior, and plugging performance were investigated. A sandpack model with multi-pressure taps was utilized to assess the microgel dispersions\u27 transport behavior and plugging efficiency. In addition, micro-optical visualization of the gel particles was deployed to study the particle size changes before and after the swelling process. Tube tests showed that micro-RPPG could be dispersed and remain as separate particles in water with a concentration below 8,000 ppm, which is a favorable concentration for gel treatment. However, during the flooding test, the amount of microgel can be entrapped in the sandpack, resulting in a higher microgel concentration (higher than 8,000 ppm), endowing the gel particles with re-crosslinking ability even with excessive water. The microgel could propagate through the sandpack model, and the required pressure gradient mainly depends on the average particle/pore ratio and gel concentration. The gel dispersion significantly reduced channel permeability, providing sufficient resistance to post-water flooding (more than 99.97 % permeability reduction). In addition, the evaluation of micro-RPPG retention revealed that it is primarily affected by both gel concentration particle/pore ratios. We have demonstrated that the novel recrosslinkable microgel can transport through large channels, but it can provide effective plugging due to its unique re-crosslinking property. However, by this property, the new microgel exhibits enhanced stability and demonstrates resistance to being flushed out in such high-permeability environments. Furthermore, with the help of novel technology, it is possible to overcome the inherited problems commonly associated with in-situ gel treatments, including chromatographic issues, low-quality control, and shearing degradation

Nonperturbative Effects in Quarkonia Associated with Large Orders in Perturbation Theory

We show that the perturbation series for quarkonia energies diverges at large orders. This results in a perturbative ambiguity in the energy that scales as e^(-1/a*Lambda) where a is the Bohr radius of quarkonium and Lambda is the QCD scale parameter. This ambiguity is associated with a nonperturbative contribution to the energy from distances of order 1/Lambda and greater. This contribution is separate from that of the gluon condensate.Comment: 6 pages, 2 figure