Causal Sufficiency Research Articles

Transcriptome data are insufficient to control false discoveries in regulatory network inference

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey

In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism underlying the data and for decision-making on effective responses or interventions. A great deal of research has been conducted to address this challenging problem from different angles. For estimating causal effect in observational data, assumptions such as Markov condition, faithfulness, and causal sufficiency are always made. Under the assumptions, full knowledge, such as a set of covariates or an underlying causal graph, is typically required. A practical challenge is that in many applications, no such full knowledge or only some partial knowledge is available. In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge. In this survey, we review these data-driven methods on causal effect estimation for a single treatment with a single outcome of interest and focus on the challenges faced by data-driven causal effect estimation. We concisely summarise the basic concepts and theories that are essential for data-driven causal effect estimation using graphical causal modelling but are scattered around the literature. We identify and discuss the challenges faced by data-driven causal effect estimation and characterise the existing methods by their assumptions and the approaches to tackling the challenges. We analyse the strengths and limitations of the different types of methods and present an empirical evaluation to support the discussions. We hope this review will motivate more researchers to design better data-driven methods based on graphical causal modelling for the challenging problem of causal effect estimation.

Causal Semantics for Implicative Verbs

Abstract Implicative verbs (e.g., manage, dare) are characterized by complement inferences (Karttunen, 1971). English manage entails its complement; the entailment reverses polarity with matrix negation, and is accompanied by a projective inference to the complement’s non-triviality (Coleman, 1975; Karttunen & Peters, 1979). I use data from Finnish and English to argue that the implicative inferential profile is derived from backgrounded relations of causal necessity and causal sufficiency (defined over the structure of a formal causal model; Pearl 2000; Schulz 2011) which link the lexical content of an implicative verb to the realization of its complement. The proposal builds on Baglini & Francez’s (2016) causal analysis of manage, but significantly revises the earlier proposal to offer a treatment which accounts not only for English manage, but extends to the lexical semantics of the full implicative class, including ‘polarity-reversing’ verbs like fail, lexically specific verbs like dare, and their Finnish counterparts. Unlike earlier analyses, the proposed causal semantics also provides a natural explanation of the commonalities between two-way entailing verbs like manage and a related class of weaker ‘one-way’ implicatives such as Finnish jaksaa (‘have the strength’), which entail complement truth values under only one matrix polarity, but generate strong pragmatic implicatures in the two-way implicative pattern under the non-entailing polarity.

Learning latent functions for causal discovery

Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions. Recent causal discovery methods focused on estimating the latent space of the data to get around a lack of causal sufficiency or additivity constraints. However, estimating the latent space significantly increases model complexity, compromising causal identifiability and making it hard to compare models that correspond to different causal hypotheses. We propose a kernel, non-parametric latent-space modelling approach and deal with the difficulty of comparing causal directions by measuring and controlling for the level of causal assumption fulfilment. We introduce a latent noise causal inference framework to estimate latent factors associated with the hypothesized causal direction by optimizing a loss function with kernel independence criteria. We extend the framework to work with time series using an additional time-dependent kernel regularizer. We discuss the additivity assumption and model complexity and give empirical evidence of performance in a wide range of synthetic and real causal discovery problems.

Causal Discovery of Flight Service Process Based on Event Sequence

The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.

Correction to: Causal Sufficiency and Actual Causation

Correction to: Causal Sufficiency and Actual Causation

PlanAlyzer

Online experiments are an integral part of the design and evaluation of software infrastructure at Internet firms. To handle the growing scale and complexity of these experiments, firms have developed software frameworks for their design and deployment. Ensuring that the results of experiments in these frameworks are trustworthy---referred to as internal validity ---can be difficult. Currently, verifying internal validity requires manual inspection by someone with substantial expertise in experimental design. We present the first approach for checking the internal validity of online experiments statically, that is, from code alone. We identify well-known problems that arise in experimental design and causal inference, which can take on unusual forms when expressed as computer programs: failures of randomization and treatment assignment, and causal sufficiency errors. Our analyses target PLANOUT, a popular framework that features a domain-specific language (DSL) to specify and run complex experiments. We have built PLANALYZER, a tool that checks PLANOUT programs for threats to internal validity, before automatically generating important data for the statistical analyses of a large class of experimental designs. We demonstrate PLANALYZER'S utility on a corpus of PLANOUT scripts deployed in production at Facebook, and we evaluate its ability to identify threats on a mutated subset of this corpus. PLANALYZER has both precision and recall of 92% on the mutated corpus, and 82% of the contrasts it generates match hand-specified data.

Causal Sufficiency and Actual Causation

Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X = x causes Y = y iff X = x is a Necessary Element of a Sufficient Set for Y = y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are due to Halpern & Pearl. Yet all of them ignore Pearl’s first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions of causal sufficiency and two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation.

Equivalent Causal Models

The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and their functional relations. In particular, I define several relations of causal ancestry and several relations of causal sufficiency, and require that the most general of these relations are preserved across equivalent models.

Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data

Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is ‘best’. This is partly because there is no agreed evaluation approach to determine their effectiveness. Moreover, each algorithm is based on a set of assumptions, such as complete data and causal sufficiency, and tend to be evaluated with data that conforms to these assumptions, however unrealistic these assumptions may be in the real world. As a result, it is widely accepted that synthetic performance overestimates real performance, although to what degree this may happen remains unknown. This paper investigates the performance of 15 state-of-the-art, well-established, or recent promising structure learning algorithms. We propose a methodology that applies the algorithms to data that incorporates synthetic noise, in an effort to better understand the performance of structure learning algorithms when applied to real data. Each algorithm is tested over multiple case studies, sample sizes, types of noise, and assessed with multiple evaluation criteria. This work involved learning approximately 10,000 graphs with a total structure learning runtime of seven months. In investigating the impact of data noise, we provide the first large scale empirical comparison of BN structure learning algorithms under different assumptions of data noise. The results suggest that traditional synthetic performance may overestimate real-world performance by anywhere between 10% and more than 50%. They also show that while score-based learning is generally superior to constraint-based learning, a higher fitting score does not necessarily imply a more accurate causal graph. The comparisons extend to other outcomes of interest, such as runtime, reliability, and resilience to noise, assessed over both small and large networks, and with both limited and big data. To facilitate comparisons with future studies, we have made all data, raw results, graphs and BN models freely available online.

Causal exclusion without causal sufficiency

Some non-reductionists claim that so-called ‘exclusion arguments’ against their position rely on a notion of causal sufficiency that is particularly problematic. I argue that such concerns about the role of causal sufficiency in exclusion arguments are relatively superficial since exclusionists can address them by reformulating exclusion arguments in terms of physical sufficiency. The resulting exclusion arguments still face familiar problems, but these are not related to the choice between causal sufficiency and physical sufficiency. The upshot is that objections to the notion of causal sufficiency can be answered in a straightforward fashion and that such objections therefore do not pose a serious threat to exclusion arguments.

Causal necessity, causal sufficiency, and the implications of causative verbs

Against past analyses, we propose that natural language causatives do not universally encode a single, unanalyzable bringing about meaning like Dowty’s (1979) CAUSE, but instead draw on an inventory of contrasting causal dependency relations. To illustrate this claim, we focus on the English causative verbs make and cause. We point out a number of differences in their inferential profiles, and argue that these follow from the fact that cause asserts a relation of causal necessity between a cause and its stated effect, while make asserts causal sufficiency. We distinguish these notions from their alethic counterparts: while causal necessity is similar to the notion of counterfactual necessity (Lewis 1973), causal sufficiency has not figured in previous analyses of causal language. We show that analyzing make as a sufficiency causative not only accounts for the similarities and differences between its distribution and that of cause, but also enables us to explain previously puzzling inferences associated with the use of make as opposed to other periphrastic causatives.

Quantum causal models via quantum Bayesianism

This paper presents a framework for quantum causal modeling based on the interpretation of causality as a relation between an observer's probability assignments to hypothetical or counterfactual experiments. The framework is based on the principle of ``causal sufficiency'': that it should be possible to make inferences about interventions using only the probabilities from a single ``reference experiment'' plus causal structure in the form of a directed acyclic graph. This leads to several interesting results: we find that quantum measurements deserve a special status distinct from interventions, and that a special rule is needed for making inferences about what would happen if they are not performed (``unmeasurements''). One natural candidate for this rule is found to be an equation of importance to the quantum Bayesianism interpretation of quantum mechanics. We find that the causal structure of quantum systems must have a ``layered'' structure, and that the model can naturally be made symmetric under reversal of the causal arrows.

PlanAlyzer: assessing threats to the validity of online experiments

Online experiments have become a ubiquitous aspect of design and engineering processes within Internet firms. As the scale of experiments has grown, so has the complexity of their design and implementation. In response, firms have developed software frameworks for designing and deploying online experiments. Ensuring that experiments in these frameworks are correctly designed and that their results are trustworthy---referred to as internal validity---can be difficult. Currently, verifying internal validity requires manual inspection by someone with substantial expertise in experimental design. We present the first approach for statically checking the internal validity of online experiments. Our checks are based on well-known problems that arise in experimental design and causal inference. Our analyses target PlanOut, a widely deployed, open-source experimentation framework that uses a domain-specific language to specify and run complex experiments. We have built a tool called PlanAlyzer that checks PlanOut programs for a variety of threats to internal validity, including failures of randomization, treatment assignment, and causal sufficiency. PlanAlyzer uses its analyses to automatically generate contrasts, a key type of information required to perform valid statistical analyses over the results of these experiments. We demonstrate PlanAlyzer's utility on a corpus of PlanOut scripts deployed in production at Facebook, and we evaluate its ability to identify threats to validity on a mutated subset of this corpus. PlanAlyzer has both precision and recall of 92% on the mutated corpus, and 82% of the contrasts it generates match hand-specified data.

A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias

Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variables and selection bias (CLS) simultaneously. I therefore introduce an algorithm called cyclic causal inference (CCI) that makes sound inferences with a conditional independence oracle under CLS, provided that we can represent the cyclic causal process as a non-recursive linear structural equation model with independent errors. Empirical results show that CCI outperforms the cyclic causal discovery algorithm in the cyclic case as well as rivals the fast causal inference and really fast causal inference algorithms in the acyclic case. An R implementation is available at https://github.com/ericstrobl/CCI .

Mining Markov Blankets Without Causal Sufficiency

Markov blankets (MBs) in Bayesian networks (BNs) play an important role in both local causal discovery and large-scale BN structure learning. Almost all existing MB discovery algorithms are designed under the assumption of causal sufficiency, which states that there are no latent common causes for two or more of the observed variables in data. However, latent common causes are ubiquitous in many applications, and hence, this assumption is often violated in practice. Thus, developing algorithms for discovering MBs without assuming causal sufficiency is of practical significance, and it is crucial for causal structure learning in real-world data. In this paper, we focus on addressing this problem. Specifically, we adopt a maximal ancestral graph (MAG) model to represent latent common causes and the concept of MBs without assuming causal sufficiency. Then, we propose an effective and efficient algorithm to discover the MB of a target variable in an MAG. Using benchmark and real-world data sets, the experiments validate the algorithm proposed in this paper.

Time in probabilistic causation: direct vs. indirect uses of lexical causative verbs

It is traditionally assumed that lexical causative verbs (e.g. kill) express direct causationonly, while periphrastic (bi-clausal) causatives (e.g. cause to die) may also express indirectcausation. In favour of this constraint, Fodor famously observed that the (change of) state introducedby lexical causative verbs is not accessible for separate adverbial modification by temporal(or manner) adverbials. In this paper, I present old and new arguments against the direct causationconstraint under the definitions of directness of Fodor and Wolff. I then propose a new definitionof directness in terms of ab-initio causal sufficiency framed in Kvart’s probabilistic account ofsingular causation. I argue that directness so redefined is an implicature rather than an entailmentof lexical causative verbs, which enables me to account for old and new data. Furthermore, I accountfor why the constraint on separate modification by temporal adverbials can be relaxed witheventuality-denoting subjects.Keywords: lexical causative verbs, direct vs. indirect causation, causal sufficiency, probabilistictheories of causation, semantics/pragmatics interface.

Validation of surrogate endpoints in cancer clinical trials via principal stratification with an application to a prostate cancer trial.

Increasing attention has been focused on the use and validation of surrogate endpoints in cancer clinical trials. Previous literature on validation of surrogate endpoints are classified into four approaches: the proportion explained approach; the indirect effects approach; the meta-analytic approach; and the principal stratification approach. The mainstream in cancer research has seen the application of a meta-analytic approach. However, VanderWeele (2013) showed that all four of these approaches potentially suffer from the surrogate paradox. It was also shown that, if a principal surrogate satisfies additional criteria called one-sided average causal sufficiency, the surrogate cannot exhibit a surrogate paradox. Here, we propose a method for estimating principal effects under a monotonicity assumption. Specifically, we consider cancer clinical trials which compare a binary surrogate endpoint and a time-to-event clinical endpoint under two naturally ordered treatments (e.g. combined therapy vs. monotherapy). Estimation based on a mean score estimating equation will be implemented by the expectation-maximization algorithm. We will also apply the proposed method as well as other surrogacy criteria to evaluate the surrogacy of prostate-specific antigen using data from a phase III advanced prostate cancer trial, clarifying the complementary roles of both the principal stratification and meta-analytic approaches in the evaluation of surrogate endpoints in cancer. Copyright © 2017 John Wiley & Sons, Ltd.

High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network.

BackgroundInferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes.ResultsWe have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain.ConclusionsWe have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed.

Principal Causal Effect Identification and Surrogate end point Evaluation by Multiple Trials

Summary Principal stratification is a causal framework to analyse randomized experiments with a post-treatment variable between the treatment and end point variables. Because the principal strata defined by the potential outcomes of the post-treatment variable are not observable, we generally cannot identify the causal effects within principal strata. Motivated by a real data set of phase III adjuvant colon cancer clinical trials, we propose approaches to identifying and estimating the principal causal effects via multiple trials. For the identifiability, we remove the commonly used exclusion restriction assumption by stipulating that the principal causal effects are homogeneous across these trials. To remove another commonly used monotonicity assumption, we give a necessary condition for the local identifiability, which requires at least three trials. Applying our approaches to the data from adjuvant colon cancer clinical trials, we find that the commonly used monotonicity assumption is untenable, and disease-free survival with 3-year follow-up is a valid surrogate end point for overall survival with 5-year follow-up, which satisfies both causal necessity and causal sufficiency. We also propose a sensitivity analysis approach based on Bayesian hierarchical models to investigate the effect of the deviation from the homogeneity assumption.