Searcharxiv⌕ Search

arXiv subjects

Santtu Tikka

Publications and source records attributed to Santtu Tikka.

23 records · Page 2Linked to original sources

The gallium anomaly reassessed using a Bayesian approach

The solar-neutrino detectors GALLEX and SAGE were calibrated by electron-neutrino flux from the $^{37}$Ar and $^{51}$Cr calibration sources. A deficit in the measured neutrino flux was recorded by counting the number of neutrino-induced conversions of the $^{71}$Ga nuclei to $^{71}$Ge nuclei. This deficit was coined ``gallium anomaly'' and it has lead to speculations about beyond-the-standard-model physics in the form of eV-mass sterile neutrinos. Notably, this anomaly has already defied final solution for more than 20 years. Here we reassess the statistical significance of this anomaly and improve the related statistical approaches by treating the neutrino experiments as repeated Bernoulli trials taking into account the fact that the number of the detected $^{71}$Ge nuclei is quite small, thus calling for a Bayesian statistical approach. In addition, we take into account the systematic errors of the experiments, their correlations, theoretical uncertainties and the number of background solar-neutrino events as a Poisson-distributed random variable. To compare with the previously reported statistical significances of the anomaly we convert the posterior intervals of our Bayesian approach to standard deviations $σ$ of the frequentist approach. We find that our approach reduces the statistical significance of the anomaly by $0.8\,σ$ for all the adopted theoretical approaches. This renders the gallium anomaly a statistically weakly supported concept. Furthermore, the implications of our approach go far beyond the gallium anomaly since the results of many rare-events experiments should be reassessed for their limited number of recorded events.

hep-ph↗

Surrogate Outcomes and Transportability

Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest. Instead of the outcome of interest, surrogate outcomes are measured in the experiments. This problem is a generalization of identifiability using surrogate experiments and we label it as surrogate outcome identifiability. We show that the concept of transportability provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.

cs.AI↗

Simplifying Probabilistic Expressions in Causal Inference

Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference. Such an expression can be obtained for an identifiable causal effect by an algorithm or by manual application of do-calculus. Often we are left with a complicated expression which can lead to biased or inefficient estimates when missing data or measurement errors are involved. We present an automatic simplification algorithm that seeks to eliminate symbolically unnecessary variables from these expressions by taking advantage of the structure of the underlying graphical model. Our method is applicable to all causal effect formulas and is readily available in the R package causaleffect.

stat.ML↗

Enhancing Identification of Causal Effects by Pruning

Causal models communicate our assumptions about causes and effects in real-world phe- nomena. Often the interest lies in the identification of the effect of an action which means deriving an expression from the observed probability distribution for the interventional distribution resulting from the action. In many cases an identifiability algorithm may return a complicated expression that contains variables that are in fact unnecessary. In practice this can lead to additional computational burden and increased bias or inefficiency of estimates when dealing with measurement error or missing data. We present graphical criteria to detect variables which are redundant in identifying causal effects. We also provide an improved version of a well-known identifiability algorithm that implements these criteria.

stat.ML↗

Identifying Causal Effects with the R Package causaleffect

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately either derives an expression for the causal distribution, or fails to identify the effect, in which case the effect is non-identifiable. In this paper, the R package causaleffect is presented, which provides an implementation of this algorithm. Functionality of causaleffect is also demonstrated through examples.

stat.ME↗