Population intervention models in causal inference

We propose a new causal parameter, which is a natural extension of existing approaches to causal inference such as marginal structural models. Modelling approaches are proposed for the difference between a treatment-specific counterfactual population distribution and the actual population distribution of an outcome in the target population of interest. Relevant parameters describe the effect of a hypothetical intervention on such a population and therefore we refer to these models as population intervention models. We focus on intervention models estimating the effect of an intervention in terms of a difference and ratio of means, called risk difference and relative risk if the outcome is binary. We provide a class of inverse-probability-of-treatment-weighted and doubly-robust estimators of the causal parameters in these models. The finite-sample performance of these new estimators is explored in a simulation study.     

Causation and causal inference for genetic effects

Over the past three decades, substantial developments have been made on how to infer the causal effect of an exposure on an outcome, using data from observational studies, with the randomized experiment as the golden standard. These developments have reshaped the paradigm of how to build statistical models, how to adjust for confounding, how to assess direct effects, mediated effects and interactions, and even how to analyze data from randomized experiments. The congruence of random transmission of alleles during meiosis and the randomization in controlled experiments/trials, suggests that genetic studies may lend themselves naturally to a causal analysis. In this contribution, we will reflect on this and motivate, through illustrative examples, where insights from the causal inference literature may help to understand and correct for typical biases in genetic effect estimates.     

Targeted maximum likelihood based causal inference: Part II

In this article, we provide a template for the practical implementation of the targeted maximum likelihood estimator for analyzing causal effects of multiple time point interventions, for which the methodology was developed and presented in Part I. In addition, the application of this template is demonstrated in two important estimation problems: estimation of the effect of individualized treatment rules based on marginal structural models for treatment rules, and the effect of a baseline treatment on survival in a randomized clinical trial in which the time till event is subject to right censoring.     

Temporal aggregation bias and inference of causal regulatory networks.

Time course experiments with microarrays have begun to provide a glimpse into the dynamic behavior of gene expression. In a typical experiment, scientists use microarrays to measure the abundance of mRNA at discrete time points after the onset of a stimulus. Recently, there has been much work on using these data to infer causal regulatory networks that model how genes influence each other. However, microarray studies typically have slow sampling rates that can lead to temporal aggregation of the signal. That is, each successive sampling point represents the sum of all signal changes since the previous sample. In this paper, we show that temporal aggregation can bias algorithms for causal inference and lead them to discover spurious relations that would not be found if the signal were sampled at a much faster rate. We discuss the implications of temporal aggregation on inference, the problems it creates, and potential directions for solutions.     

Critical reasoning on causal inference in genome-wide linkage and association studies

Genome-wide linkage and association studies of tens of thousands of clinical and molecular traits are currently underway, offering rich data for inferring causality between traits and genetic variation. However, the inference process is based on discovering subtle patterns in the correlation between traits and is therefore challenging and could create a flood of untrustworthy causal inferences. Here we introduce the concerns and show that they are already valid in simple scenarios of two traits linked to or associated with the same genomic region. We argue that more comprehensive analysis and Bayesian reasoning are needed and that these can overcome some of the pitfalls, although not in every conceivable case. We conclude that causal inference methods can still be of use in the iterative process of mathematical modeling and biological validation.     

Advances to Bayesian network inference for generating causal networks from observational biological data

MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/     

An application of collaborative targeted maximum likelihood estimation in causal inference and genomics

A concrete example of the collaborative double-robust targeted likelihood estimator (C-TMLE) introduced in a companion article in this issue is presented, and applied to the estimation of causal effects and variable importance parameters in genomic data. The focus is on non-parametric estimation in a point treatment data structure. Simulations illustrate the performance of C-TMLE relative to current competitors such as the augmented inverse probability of treatment weighted estimator that relies on an external non-collaborative estimator of the treatment mechanism, and inefficient estimation procedures including propensity score matching and standard inverse probability of treatment weighting. C-TMLE is also applied to the estimation of the covariate-adjusted marginal effect of individual HIV mutations on resistance to the anti-retroviral drug lopinavir. The influence curve of the C-TMLE is used to establish asymptotically valid statistical inference. The list of mutations found to have a statistically significant association with resistance is in excellent agreement with mutation scores provided by the Stanford HIVdb mutation scores database.     

Genetic algorithms and parallel processing in maximum-likelihood phylogeny inference

We investigated the usefulness of a parallel genetic algorithm for phylogenetic inference under the maximum-likelihood (ML) optimality criterion. Parallelization was accomplished by assigning each "individual" in the genetic algorithm "population" to a separate processor so that the number of processors used was equal to the size of the evolving population (plus one additional processor for the control of operations). The genetic algorithm incorporated branch-length and topological mutation, recombination, selection on the ML score, and (in some cases) migration and recombination among subpopulations. We tested this parallel genetic algorithm with large (228 taxa) data sets of both empirically observed DNA sequence data (for angiosperms) as well as simulated DNA sequence data. For both observed and simulated data, search-time improvement was nearly linear with respect to the number of processors, so the parallelization strategy appears to be highly effective at improving computation time for large phylogenetic problems using the genetic algorithm. We also explored various ways of optimizing and tuning the parameters of the genetic algorithm. Under the conditions of our analyses, we did not find the best-known solution using the genetic algorithm approach before terminating each run. We discuss some possible limitations of the current implementation of this genetic algorithm as well as of avenues for its future improvement.     

Dynamic causal models and physiological inference: a validation study using isoflurane anaesthesia in rodents

Generative models of neuroimaging and electrophysiological data present new opportunities for accessing hidden or latent brain states. Dynamic causal modeling (DCM) uses Bayesian model inversion and selection to infer the synaptic mechanisms underlying empirically observed brain responses. DCM for electrophysiological data, in particular, aims to estimate the relative strength of synaptic transmission at different cell types and via specific neurotransmitters. Here, we report a DCM validation study concerning inference on excitatory and inhibitory synaptic transmission, using different doses of a volatile anaesthetic agent (isoflurane) to parametrically modify excitatory and inhibitory synaptic processing while recording local field potentials (LFPs) from primary auditory cortex (A1) and the posterior auditory field (PAF) in the auditory belt region in rodents. We test whether DCM can infer, from the LFP measurements, the expected drug-induced changes in synaptic transmission mediated via fast ionotropic receptors; i.e., excitatory (glutamatergic) AMPA and inhibitory GABA(A) receptors. Cross- and auto-spectra from the two regions were used to optimise three DCMs based on biologically plausible neural mass models and specific network architectures. Consistent with known extrinsic connectivity patterns in sensory hierarchies, we found that a model comprising forward connections from A1 to PAF and backward connections from PAF to A1 outperformed a model with forward connections from PAF to A1 and backward connections from A1 to PAF and a model with reciprocal lateral connections. The parameter estimates from the most plausible model indicated that the amplitude of fast glutamatergic excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) behaved as predicted by previous neurophysiological studies. Specifically, with increasing levels of anaesthesia, glutamatergic EPSPs decreased linearly, whereas fast GABAergic IPSPs displayed a nonlinear (saturating) increase. The consistency of our model-based in vivo results with experimental in vitro results lends further validity to the capacity of DCM to infer on synaptic processes using macroscopic neurophysiological data.     

Information processing in large-scale cerebral networks: the causal connectivity approach

Today, cognitive functions are considered to be the offspring of the activity of large-scale networks of functionally interconnected cerebral regions. The interpretation of cerebral activation data provided by functional imaging has therefore recently moved to the search for the effective connectivity of activated regions, which aims at understanding the role of anatomical links in the activation propagation. Our assumption is that only causal connectivity can offer a real understanding of the links between brain and mind. Causal connectivity is based on the anatomical connection pattern, the information processing within cerebral regions and the causal influences that connected regions exert on each other. In our approach, the information processing within a region is implemented by a causal network of functional primitives, which are the interpretation of integrated biological properties. Our choice of a qualitative representation of information reflects the fact that cerebral activation data are only the approximate view, provided by imaging techniques, of the real cerebral activity. This explicit modeling approach allows the formulation and the simulation of functional and physiological assumptions about activation data. Two alternative models explaining results of the striate cortex activation described by Fox and Raichle (Fox PT, Raichle ME (1984) J. Neurophysiol 51:1109–1120; Fox PT, Raichle ME (1985) Ann Neurol 17:303–305) are provided as an example of our approach. Received: 22 December 1998 / Accepted in revised form: 23 June 1999     

Gene-environment interaction testing in family-based association studies with phenotypically ascertained samples: a causal inference approach

We propose a method for testing gene-environment (G × E) interactions on a complex trait in family-based studies in which a phenotypic ascertainment criterion has been imposed. This novel approach employs G-estimation, a semiparametric estimation technique from the causal inference literature, to avoid modeling of the association between the environmental exposure and the phenotype, to gain robustness against unmeasured confounding due to population substructure, and to acknowledge the ascertainment conditions. The proposed test allows for incomplete parental genotypes. It is compared by simulation studies to an analogous conditional likelihood-based approach and to the QBAT-I test, which also invokes the G-estimation principle but ignores ascertainment. We apply our approach to a study of chronic obstructive pulmonary disorder.     

Neural processing at the speed of smell

Olfaction is typically described as behaviorally slow, suggesting neural processes on the order of hundreds of milliseconds to seconds as candidate mechanisms in the creation of olfactory percepts. Whereas a recent study challenged this view in suggesting that a single sniff was sufficient for optimal olfactory discrimination, a study by Abraham et al. in this issue of Neuron sets out to negate the challenge by demonstrating increased processing time for discrimination of similar versus dissimilar stimuli. Here we reconcile both studies, which in our view together support the notion of a speed-accuracy tradeoff in olfactory discriminations that are made within about 200 ms. These findings are discussed in light of the challenges related to defining olfactory perceptual similarity in nonhuman animals.     

Reducing the variability in cDNA microarray image processing by Bayesian inference

MOTIVATION: Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray 'grids'. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray 'spots', capturing uncertainty that can be passed to downstream analysis. RESULTS: Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers' time normally spent in refining the grid placement.