Abstract
Causal pluralism can be defended not only in respect to causal concepts and methodological guidelines, but also at the finer-grained level of causal inference from a particular source of evidence for causation. An argument for this last variety of pluralism is made based on an analysis of causal inference from randomized experiments (RCTs). Here, the causal interpretation of a statistically significant association can be established via multiple paths of reasoning, each relying on different assumptions and providing distinct elements of information in favour of a causal interpretation.
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Notes
The proposed reconstruction concerns explanatory, non-adaptive RCTs conducted under the assumption that no prior knowledge relevant to the causal claim under investigation is available. This type of experiment is common in clinical and preclinical research, psychology, education, agriculture and ecology. Since the experimental design behind traditional RCTs is meant to accommodate statistical inference under a frequentist approach, the frequentist statistics standardly employed in the analysis of experimental results is assumed in this paper.
The above inference, known as the randomization inference, does not rely on theoretical approximations based on assumptions about the shape of the sampling distribution. In practice, researchers are often interested in demonstrating that there is an average between-group exposure association, as well as estimating the size of that association. Thus, “the first step in assessing the intervention’s effect involves testing for a statistical association between intervention group membership (intervention vs. control) and the identified outcome […]. This is accomplished by using an appropriate inferential statistical procedure (e.g., an independent-samples t test) coupled with an effect size estimate (e.g., Cohen’s d), to provide pertinent information regarding both the statistical significance and strength (i.e., the amount of benefit) of the intervention–outcome association” (DeLucia and Pitts 2010, p. 631). Unlike the randomization inference, t tests presuppose that the outcomes are normally distributed or that the population from which individuals are drawn is large enough that the test statistics follow a posited sampling distribution (Gerber and Green 2012, p. 65).
“When a variable systematically varies with the independent variable, the confounding variable provides an explanation other than the independent variable for changes in the dependent variable” (Kovera 2010b, p. 220). For instance, in a linear regression model, the “[c]orrelation of the random effects εj [the error term in µj = α + xβ + εj] with the treatments xj [allocation] leads to bias in estimating β [the magnitude of the contribution of x to µ]. This bias may be attributed to or interpreted as confounding for β in the regression analysis. Confounders are now covariates that ‘explain’ the correlation between εj and xj. In particular, confounders reduce the correlation of xj and εj when entered in the model and so reduce the bias in estimating β” (Greenland et al. 1999, p. 34). Note that confounding is defined here in strictly statistical terms with no explicit reference to causation. In practice, researchers assess the potential for confounding by checking if allocation alone (before exposure to the tested treatment) covaries with potential confounders; in other words, researchers check if usual suspects, such as gender and age, are unbalanced between test and control trial participants. If unbalances exist, then it is possible that the allocation intervention sets both the value of the independent variable X to x and that of covariate W to w, such that the observed differences in outcome can be explained not only by differences in X, but also by differences in W.
Randomization cannot and is not meant to guarantee that variables will not happen to be associated with groups by chance. That this is the case is most vividly illustrated by a trial in which only two individuals are randomly assigned to test and control conditions: in this case, individual characteristics and allocation condition are undistinguishable. It can certainly be argued that random assignment “equates groups on expectation at pretest,” that is, “in the long run over many randomized experiments” (Shadish et al. 2002, p. 250). However, this “does not mean that random assignment equates units on observed pretest scores” (Shadish et al. 2002, p. 250). For any given round of randomization, gains in precision are driven by group size. If “the original sample is large enough then the two groups should be more or less identical in the important characteristics […]. The two groups will differ only by chance” (Bowers 2014, p. 120). Since random error decreases as sample size increases, it follows that as “the sample size grows, observed and unobserved confounders are balanced across treatment and control groups with arbitrarily high probability” (Sekhon 2008, p. 273). Nevertheless, even in the case of large groups, a single round of randomization cannot guarantee perfect balancing, but only a high probability that known and unknown confounders are balanced across treatment and control group.
Greenland et al. (1999, p. 29) take this to be the oldest usage of confounding, namely as “a type of bias in estimating causal effects. This bias is sometimes informally described as a mixing of effects of extraneous factors (called confounders) with the effect of interest.” According to this usage a “variable cannot be a confounder (in this sense) unless (1) it can causally affect the outcome parameter μ within treatment groups, and (2) it is distributed differently among the compared populations […]. The two necessary conditions (1) and (2) are sometimes offered together as a definition of a confounder” (Greenland et al. (1999, p. 33–34). Greenland et al. (1999, p. 33) further point out that confounding occurs only if different distributions (unbalances) result in net effect differences in the outcome of interest: “a covariate difference […] is a necessary but not sufficient condition for confounding, because the effects of the various covariate differences may balance out in such a way that no confounding is present.”.
On the other hand, if comparability is understood as a homogenization removing all unbalances, including those due to random error, then something is amiss. The fact that a chance explanation is considered and needs to be ruled out by a statistical test indicates that the causal inference deployed in the context of an RCT works explicitly on the expectation that there is a non-negligible risk of generating incomparable groups and that, therefore, comparability cannot be guaranteed. Senn points out that not only “[i]t is not necessary for the groups to be balanced,” but “the probability calculation applied to a clinical trial automatically makes an allowance for the fact that groups will almost certainly be unbalanced, and if one knew that they were balanced, then the calculation that is usually performed would not be correct” (Senn 2013, p. 1442). Conversely, prior assurance of comparability removes the need to consider the possibility of a chance explanation and, therefore, the need to perform a statistical test: “If treatment groups could be equated before treatment, and if they were different after treatment, then pretest selection differences could not be a cause of observed posttest differences” (Shadish et al. 2002, p. 249). Likewise, if we knew that all patients and their circumstances are identical, the recovery of every single patient in the test group could only be attributed to the efficacy of the treatment (Hill 1955, Ch. VIII). Sekhon (2008) further argues that, under these circumstances, causal inference collapses into a version of Mill’s method of difference. Given a zero probability of generating incomparable groups, if differences in outcomes are observed, these differences must have been caused by something other than the process by which the groups were generated (random allocation) irrespective of the magnitude of the differences in outcomes and the size of the groups. Presumably, this is the strategy favoured in most controlled experiments in basic science, where it is common practice to engineer homogenous populations of comparable individuals, for instance, by generating immortalized cell-lines consisting of clones or systematically inbreeding mice in order to produce isogenic/homozygous stains (Baetu 2020; Müller-Wille 2007).
Some authors prefer the term ‘internal validity’ in order to emphasize that the results of the test concern solely the particular experimental setup employed in the study (e.g., trial participants, the specific version of the intervention employed, the circumstances in which the intervention was conducted, the specific outcomes or endpoints assessed in the experiment, etc.) (Manski 2007; Shadish et al. 2002).
In principle, the prospective design of experiments may also help rule out non-causal explanations of systematic association, such as supervenience, coreference and nomic dependence scenarios, in which the independent and dependent variables covary simultaneously.
A common example is gene duplication. If, upon knocking out a gene, no differences in phenotype are observed, background knowledge dictates that this result is more likely to be due to the compensating effect of homologous sequences than to the fact that the gene plays no causal role vis-à-vis phenotype.
Observational data can discriminate between two classes of Markov-equivalent structures, with chains, reverse chains and forks, in one class, and colliders (A→B←C), in the other, thus allowing for causal inference from observational data assuming minimality, Markov and positivity (Pearl 2000; Spirtes et al. 1993). Despite its importance for causal discovery algorithms, this result is not of significant utility for causal inference from experiments which probe solely the input and output of a system, thus generating data about the conditional probability of the output variable given the probability of the input variable.
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This research was supported by SSHRC Grant # 430-2020-0654.
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Baetu, T.M. Inferential Pluralism in Causal Reasoning from Randomized Experiments. Acta Biotheor 70, 22 (2022). https://doi.org/10.1007/s10441-022-09446-2
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DOI: https://doi.org/10.1007/s10441-022-09446-2