Estimating the effect of joint interventions from observational data in sparse high-dimensional settings

We consider the estimation of joint causal effects from observational data. In particular, we propose new methods to estimate the effect of multiple simultaneous interventions (e.g., multiple gene knockouts), under the assumption that the observational data come from an unknown linear structural equation model with independent errors. We derive asymptotic variances of our estimators when the underlying causal structure is partly known, as well as high-dimensional consistency when the causal structure is fully unknown and the joint distribution is multivariate Gaussian. We also propose a generalization of our methodology to the class of nonparanormal distributions. We evaluate the estimators in simulation studies and also illustrate them on data from the DREAM4 challenge.Comment: 30 pages, 3 figures, 45 pages supplemen

Illusions of causality: How they bias our everyday thinking and how they could be reduced

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion

Illusions of causality: How they bias our everyday thinking and how they could be reduced

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion

Fallacious beliefs: gambling specific and belief in the paranormal

This thesis presents a series of studies investigating two types of fallacious belief: gambling fallacies (GF) and paranormal beliefs (PB). The first study identifies the errors that constitute GF and critically evaluates instruments designed to measure them. The second study re-evaluates the etiological role of GF in problem gambling using an improved instrument in a longitudinal dataset with results indicating that GF do contribute to problematic gambling, but to a lesser extent than previously supposed and in a bidirectional manner. The third study examines factors associated with susceptibility to GF and compares them to those associated with PB. A very similar set were predictive of both, with most being inherently malleable. The final study, a systematic review, evaluates the efficacy of interventions designed to reduce PB with results indicating that direct examination of the evidentiary basis of PB appears to be effective, although the generalizability of this effect remains uncertain.Alberta Gambling Research Institute; Social Sciences and Humanities Council (SSHRC

Modelos estadísticos aplicados al análisis de los fenómenos inexplicados y a las creencias pseudocientíficas en psicología

Les creences pseudocientífiques es poden definir com l'acte d'acceptar l'existència real de fenòmens o informació que no té evidències científiques contrastables i/o sòlides. Aquesta expressió és generalista i inclou diversos subtipus de creences, com les creences paranormals. En canvi, els fenòmens inexplicats o anòmals fan referència a comportaments que desafien els límits de el coneixement científic actual i són difícils d'explicar en termes psicològics. Aquest és el cas de la "recepció anòmala de la informació" (RAI). Els objectius d'aquesta investigació es van centrar en el mesurament, en la predicció i explicació psicològica dels fenòmens anòmals i de les creences pseudocientífiques. Es van aplicar diversos models estadístics centrats en proves de contrast d'hipòtesis, models d'equacions estructurals i inferències bayesianes. Es va desenvolupar un nou instrument d'avaluació denominat Multivariable Multiaxial Suggestibility Inventory -2 (MMSI-2) i es van analitzar les seves propietats psicomètriques amb una mostra de 3.224 subjectes. Aquest qüestionari examina experiències o fenòmens anòmals, trets de la personalitat, riscos psicopatològics i també inclou diversos indicadors conductuals per a la detecció de mentides. També es van contrastar els efectes o les ocurrències de la RAI (com a exemple de fenomen anòmal) i es va analitzar l'impacte psicosocial de les creences pseudocientífiques durant les diferents etapes de la crisi del coronavirus. Els resultats van permetre acceptar satisfactòriament la validesa i fiabilitat de l'MMSI-2. Els encerts de les proves experimentals de la RAI no van superar el atzar estimat. Es va concloure que no es van obtenir evidències estadístiques a favor de la RAI. Les creences pseudocientífiques, els fenòmens inexplicats i els símptomes positius de les psicosis van augmentar després del primer confinament social per coronavirus. Es va concloure que els residents en municipis urbans presentaven major quantitat de creences pseudocientífiques que els residents en zones rurals. Es discuteix sobre la implicació dels estils d'afrontament, la teoria de la marginalitat social i la teoria de l'MMSI-2 com a possibles models relacionats amb l'explicació dels fenòmens anòmals i de les creences pseudocientífiques.Las creencias pseudocientíficas pueden definirse como el acto de aceptar la existencia real de fenómenos o información que carece de evidencias científicas contrastables y/o sólidas. Esta expresión es generalista e incluye varios subtipos de creencias, como las creencias paranormales. En cambio, los fenómenos inexplicados o anómalos hacen referencia a comportamientos que desafían los límites del conocimiento científico actual y son difíciles de explicar en términos psicológicos. Este es el caso de la “recepción anómala de la información” (RAI). Los objetivos de esta investigación se centraron en la medición estadística, predicción y explicación psicológica de los fenómenos anómalos y de las creencias pseudocientíficas. Se aplicaron varios modelos estadísticos centrados en pruebas de contraste de hipótesis, modelos de ecuaciones estructurales e inferencias bayesianas. Se desarrolló un nuevo instrumento de evaluación denominado Multivariable Multiaxial Suggestibility Inventory -2 (MMSI-2) y se analizaron sus propiedades psicométricas con una muestra de 3.224 sujetos. Este cuestionario examina experiencias o fenómenos anómalos, rasgos de la personalidad, riesgos psicopatológicos y también incluye varios indicadores conductuales para la detección de mentiras. También se contrastaron los efectos o las ocurrencias de la RAI (como ejemplo de fenómeno anómalo) y se analizó el impacto psicosocial de las creencias pseudocientíficas durante las distintas etapas de la crisis del coronavirus. Los resultados permitieron aceptar satisfactoriamente la validez y fiabilidad del MMSI-2. Los aciertos de las pruebas experimentales de la RAI no superaron el azar estimado. Se concluyó que no se obtuvieron evidencias estadísticas a favor de la RAI. Las creencias pseudocientíficas, los fenómenos inexplicados y los síntomas positivos de las psicosis aumentaron tras el primer confinamiento social por coronavirus. Se concluyó que los residentes en municipios urbanos presentaban mayor cantidad de creencias pseudocientíficas que los residentes en zonas rurales. Se discute sobre la implicación de los estilos de afrontamiento, la teoría de la marginalidad social y la teoría del MMSI-2 como posibles modelos relacionados con la explicación de los fenómenos anómalos y de las creencias pseudocientíficas.Pseudoscientific beliefs can be defined as the act of accepting the existence of phenomena or information that lacks contrastable and/or solid scientific evidence. This generalist approach includes several subtypes of beliefs, such as paranormal beliefs. In contrast, unexplained or anomalous phenomena refer to behaviors that defy the limits of current scientific knowledge and are difficult to explain in psychological terms. This is the case of the "anomalous reception of information" (ARI). This research focused on the statistical measurement, prediction and psychological explanation of anomalous phenomena and pseudoscientific beliefs. Several statistical models were applied focusing on hypothesis testing, structural equation modeling and Bayesian inference. A new assessment instrument called Multivariable Multiaxial Suggestibility Inventory -2 (MMSI-2) was developed and its psychometric properties were analyzed with a sample of 3,224 subjects. This questionnaire examined anomalous experiences or phenomena, personality traits, psychopathological risks and also included several behavioral indicators for lie detection. The effects or occurrences of ARI (as an example of an anomalous phenomenon) were also contrasted and the psychosocial impact of pseudoscientific beliefs during the different stages of the coronavirus crisis was analyzed. The results allowed us to satisfactorily accept the validity and reliability of the MMSI-2. The hits on the ARI experimental tests did not exceed the estimated chance. It was concluded that no statistical evidence was obtained in favor of the ARI. Pseudoscientific beliefs, unexplained phenomena and positive symptoms of psychosis increased after the first coronavirus social confinement. It was concluded that residents in urban municipalities had a higher number of pseudoscientific beliefs than residents in rural areas. The implication of coping styles, the social marginality theory and the MMSI-2 theory as possible models related to the explanation of anomalous phenomena and pseudoscientific beliefs is discussed

Graphical model inference with external network data

We consider two applications where we study how dependence structure between many variables is linked to external network data. We first study the interplay between social media connectedness and the co-evolution of the COVID-19 pandemic across USA counties. We next study study how the dependence between stock market returns across firms relates to similarities in economic and policy indicators from text regulatory filings. Both applications are modelled via Gaussian graphical models where one has external network data. We develop spike-and-slab and graphical LASSO frameworks to integrate the network data, both facilitating the interpretation of the graphical model and improving inference. The goal is to detect when the network data relates to the graphical model and, if so, explain how. We found that counties strongly connected on Facebook are more likely to have similar COVID-19 evolution (positive partial correlations), accounting for various factors driving the mean. We also found that the association in stock market returns depends in a stronger fashion on economic than on policy indicators. The examples show that data integration can improve interpretation, statistical accuracy, and out-of-sample prediction, in some instances using significantly sparser graphical models