Search results for "kausaliteetti"
showing 10 items of 43 documents
The Strategic Reference Gene: an organismal theory of inclusive fitness
2019
How to define and use the concept of inclusive fitness is a contentious topic in evolutionary theory. Inclusive fitness can be used to calculate selection on a focal gene, but it is also applied to whole organisms. Individuals are then predicted to appear designed as if to maximise their inclusive fitness, provided that certain conditions are met (formally when interactions between individuals are 'additive'). Here we argue that applying the concept of inclusive fitness to organisms is justified under far broader conditions than previously shown, but only if it is appropriately defined. Specifically, we propose that organisms should maximise the sum of their offspring (including any accrued…
DNA methylation links prenatal smoking exposure to later life health outcomes in offspring
2019
Background Maternal smoking during pregnancy is associated with adverse offspring health outcomes across their life course. We hypothesize that DNA methylation is a potential mediator of this relationship. Methods We examined the association of prenatal maternal smoking with offspring blood DNA methylation in 2821 individuals (age 16 to 48 years) from five prospective birth cohort studies and perform Mendelian randomization and mediation analyses to assess whether methylation markers have causal effects on disease outcomes in the offspring. Results We identify 69 differentially methylated CpGs in 36 genomic regions (P value < 1 × 10−7) associated with exposure to maternal smoking in adolesc…
Physical Activity, Sleep, and Symptoms of Depression in Adults - Testing for Mediation
2019
Abstract Purpose: Physical activity, sleep problems, and symptoms of depression contribute to overall well-being. The factors are reciprocally associated, but the nature of these associations remains unclear. The present study examined whether sleep problems mediated the association between physical activity and depressive symptoms. Methods: The eligible population (n = 3596) consisted of adults from the ongoing, population-based Cardiovascular Risk in Young Finns Study started in 1980. Participants’ leisure-time physical activity was assessed with physical activity index (2007) and sleep problems with Jenkins’ Sleep Questionnaire in 2007 and 2011. Depressive symptoms were measured using mo…
A Dialectical Reading of Dynamic Systems Theory : Transcending Socialized Cognition and Cognized Social Dualism in L2 Studies
2016
Dynamic systems theory (DST) has affordances to be a quintessential metatheoretical architecture for the nuancing of the time-locked mechanisms and processes of the L2 system. The received construal of DST in L2 studies presumes the emergence of structural regularities and the cognitive organization of the L2 system as simply a function of lower-level language use in social milieux. Critiquing some of the bedrock assumptions anchoring the extant reading, this article sketches a complementary dialectical construal of DST. Explicating circular causality, a nexus of causality types, and self-organizational emergence and their attendant implications for an adequate description and explanation o…
Simplifying Probabilistic Expressions in Causal Inference
2018
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 …
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach
2021
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to generality of do-calculus, the search is capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and…
Estimation of causal effects with small data in the presence of trapdoor variables
2021
We consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that when an identifiable causal effect is subject to an implicit functional constraint that is not deducible from conditional independence relations, the estimator of the causal effect can exhibit bias in small samples. This bias is related to variables that we call trapdoor variables. We use simulated data to study different strategies to account for trapdoor variables and suggest how the related trapdoor bias might be minimized. The importance of trapdoor variables in causal effect estimation is illustrated with rea…
Identifying Causal Effects via Context-specific Independence Relations
2019
Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces context-specific independence (CSI) relations, the existing identification procedures and criteria based on do-calculus are inherently incomplete. We show that deciding causal effect non-identifiability is NP-hard in the presence of CSIs. Motivated by this, we design a calculus and an automated search procedure for identifying causal effects in the presence of CSIs. The approach is provably sound and it includes standard do-calculus as a special case. With the approach we can …
Study design in causal models
2012
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions whether a causal or observational relationship can be estimated from the collect…
Surrogate outcomes and transportability
2019
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.