Counterfactual Theory Research Articles

Explaining the 2003 Iraq war (again) - Gore-war vs. Gore-peace revisited

ABSTRACT I offer a response to Professor Joseph Stieb’s article entitled ’Rethinking Gore-War: Counterfactuals and the 2003 Iraq War’. In his critique of my book, Explaining the Iraq War: Counterfactual Theory, Logic and Evidence, Stieb claims that neoconservatives and right-wing hawks in the Bush administration were singularly responsible for mobilizing domestic and international support for the US–UK invasion based on fabricated threats about Iraq’s WMD and links to 9/11, Al-Qaeda and globalized terrorism. The central objective of my book (and this reply) is to refute and repudiate Stieb’s fundamentally flawed and simplistic ’neoconist’ account of the 2003 invasion.

Modeling confidence in causal judgments.

Counterfactual theories propose that people's capacity for causal judgment depends on their ability to consider alternative possibilities: The lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate a variety of psychological effects on causal judgment, a range of recent accounts have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded measure of causal strength. While such models successfully describe the influence of the statistical normality (i.e., the base rate) of the candidate and alternate causes on causal judgments, we show that they make further untested predictions about how normality influences people's confidence in their causal judgments. In a large (N = 3,020) sample of participants in a causal judgment task, we found that normality indeed influences people's confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model in which people are more confident in a causal relationship when the effect of the cause is less variable among imagined counterfactual possibilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Looking at Mental Images: Eye-Tracking Mental Simulation During Retrospective Causal Judgment.

How do people evaluate causal relationships? Do they just consider what actually happened, or do they also consider what could have counterfactually happened? Using eye tracking and Gaussian process modeling, we investigated how people mentally simulated past events to judge what caused the outcomes to occur. Participants played a virtual ball-shooting game and then-while looking at a blank screen-mentally simulated (a) what actually happened, (b) what counterfactually could have happened, or (c) what caused the outcome to happen. Our findings showed that participants moved their eyes in patterns consistent with the actual or counterfactual events that they mentally simulated. When simulating what caused the outcome to occur, participants moved their eyes consistent with simulations of counterfactual possibilities. These results favor counterfactual theories of causal reasoning, demonstrate how eye movements can reflect simulation during this reasoning and provide a novel approach for investigating retrospective causal reasoning and counterfactual thinking.

Introduction to target trial emulation in rehabilitation: a systematic approach to emulate a randomized controlled trial using observational data.

Rehabilitation providers and policymakers need valid evidence to make informed decisions about the healthcare needs of the population. Whenever possible, these decisions should be informed by randomized controlled trials (RCTs). However, there are circumstances when evidence needs to be generated rapidly, or when RCTs are not ethical or feasible. These situations apply to studying the effects of complex interventions, including rehabilitation as defined by Cochrane Rehabilitation. Therefore, we explore using the target trial emulation framework by Hernán and colleagues to obtain valid estimates of the causal effects of rehabilitation when RCTs cannot be conducted. Target trial emulation is a framework guiding the design and analysis of non-randomized comparative effectiveness studies using observational data, by emulating a hypothetical RCT. In the context of rehabilitation, we outline steps for applying the target trial emulation framework using real world data, highlighting methodological considerations, limitations, potential mitigating strategies, and causal inference and counterfactual theory as foundational principles to estimating causal effects. Overall, we aim to strengthen methodological approaches used to estimate causal effects of rehabilitation when RCTs cannot be conducted.

Causal decision theory, context, and determinism

AbstractThe classic formulation of causal decision theory (CDT) appeals to counterfactuals. It says that you should aim to choose an option that would have a good outcome, were you to choose it. However, this version of CDT faces trouble if the laws of nature are deterministic. After all, the standard theory of counterfactuals says that, if the laws are deterministic, then if anything—including the choice you make—were different in the present, either the laws would be violated or the distant past would be changed. And as several authors have shown, it's easy to transform this upshot of the standard theory of counterfactuals into full‐blown counterexamples to CDT. In response to these counterexamples, I argue here that the problem lies, not so much with CDT's guiding idea—that it's the expected causal consequences of your actions that matter for rational decision‐making—but with the fact that the classic formulation of CDT doesn't pay sufficient attention to the context‐sensitivity of counterfactuals. I develop a contextualist version of CDT which better accounts for this context‐sensitivity. And I show that my theory avoids the problems faced by the classic formulation of CDT in determinstic worlds.

Some Explanatory Issues with Woodward’s Notion of Intervention

ABSTRACT James Woodward’s manipulationist counterfactual theory of explanation offers strong tools for an adequate approach to explanation endeavours. One of these tools is the notion of intervention, which serves as a guiding principle for identifying explanations as causal, thus preserving the unidirectionality of explanatory praxis. Nevertheless, in this paper, I argue that in some cases of explanation, this notion has a rather redundant role since it is either impossible to define or it can be replaced by other types of manipulations or both. I shall show these shortcomings of intervention in two particularly detailed analyses of an economic example pertaining to Okun’s law and the pendulum explanation. In analysing the former example, I contend that given the complex causal situation of economic phenomena, the notion of intervention is inept at helping explain them. In the latter example, I show that the notion of intervention is readily replaceable by other types of variation—conceptual or mathematical. Finally, I shall end this article by examining the case of pendulum explanation in light of Woodward’s new approach to explanation direction. In the end, the conclusion is that the variables of the pendulum law are in a symmetrical relationship.

Determinism, Counterfactuals, and the Possibility of Time Travel

The Consequence argument is an argument from plausible premises–our lack of causal power over the laws and past–to an implausible conclusion: that if determinism is true, we are equally powerless with respect to the future. What the compatibilist needs is a theory of counterfactuals that preserves the links between counterfactuals, causation, and the natural laws in a way that supports our commonsense belief that we have the power to make a causal difference to the future but no such power with respect to the past. Lewis’s critique of the Consequence argument was based on his theory of counterfactuals and his analysis of causation as a counterfactual relation between particular events. He argued that, at a world that is deterministic in the way that ours might be, counterfactuals are temporally asymmetric in a way that matches the contingent temporal asymmetry of cauation. So it is not surprising, but only to be expected, that the past is causally closed while the future is causally open. If this worked, it would be just what the compatibilist needs. But it doesn’t work. There is an argument, due to Tooley and recently endorsed by Wasserman, that a fundamental feature of Lewis’s theory of counterfactuals is inconsistent with the metaphysical possibility of time travel and backwards causation. If this is right, then Lewis’s response to the Consequence argument fails. I endorse this conclusion, but argue that there is a better theory of counterfactuals–a theory that leaves open the metaphysical possibility of time travel to the past and backwards causation.

Counterfactuals and the logic of causal selection.

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather … )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Backwards Time Travel and Backwards Causation

Is it possible to travel back in time? Objections against backwards time travel frequently focus on the impossibility of changing the past. However, there is a deeper problem for backwards time travel. It seems that if backwards time travel were possible, the time traveler could causally affect the past, even if she could not alter the past. Is it possible to causally affect the past? David Lewis famously argues that backwards causation is possible as long as we understand causal dependence in terms of counterfactual dependence rather than temporal precedence. In this paper, I argue that this Lewisian line of reasoning is untenable. Lewis counterfactual theory of causation faces the so-called problem of effects. To solve this problem, he either has to reintroduce temporal precedence back into the definition of causation or stipulate that the closest possible world needs to be historically the same as the actual one. Either way, the possibility of backwards causation is immediately ruled out.

Counterfactual-based minority oversampling for imbalanced classification

A key challenge of oversampling in imbalanced classification is that the generation of new minority samples often neglects the usage of majority classes, resulting in most new minority sampling spreading the whole minority space. In view of this, we present a new oversampling framework based on the counterfactual theory. Our framework introduces a counterfactual objective by leveraging the rich inherent information of majority classes and explicitly perturbing majority samples to generate new samples in the territory of minority space. It can be analytically shown that the new minority samples satisfy the minimum inversion. Therefore, most of them are located near the decision boundary. The empirical evaluation of the six benchmark datasets shows that our approach clearly outperforms the state-of-the-art methods.

Inverse Probability Weighting to Estimate Exposure Effects on the Burden of Recurrent Outcomes in the Presence of Competing Events.

Recurrent events-outcomes that an individual can experience repeatedly over the course of follow-up-are common in epidemiologic and health services research. Studies involving recurrent events often focus on time to first occurrence or on event rates, which assume constant hazards over time. In this paper, we contextualize recurrent event parameters of interest using counterfactual theory in a causal inference framework and describe an approach for estimating a target parameter referred to as the mean cumulative count. This approach leverages inverse probability weights to control measured confounding with an existing (and underutilized) nonparametric estimator of recurrent event burden first proposed by Dong et al. in 2015. We use simulations to demonstrate the unbiased estimation of the mean cumulative count using the weighted Dong-Yasui estimator in a variety of scenarios. The weighted Dong-Yasui estimator for the mean cumulative count allows researchers to use observational data to flexibly estimate and contrast the expected number of cumulative events experienced per individual by a given time point under different exposure regimens. We provide code to ease application of this method.

WHY I BELIEVE IN MONSTERS (AND YOU SHOULD TOO)

According to Kaplan’s bidimensional theory of demonstratives, the descriptive content of any indexical term (and the sentences they appear in) is only employed to determine its reference in any possible world rigidly but cannot be expressed by the sentence’s truth conditions. Kaplan then argues that an indexical sentence’s informativeness depends on what he calls its character, a property of the context that relates a particular context to a concrete content, but it cannot be a part of the proposition the sentence entertains (its content), primarily given the logical inconsistencies the opposite would show in the theory of conditionals and counterfactuals. I agree with Kaplan that indexicals should not be considered disguised descriptions. Nevertheless, I believe that their content is informative and, therefore, part of the proposition these sentences express, even though that implies accepting the existence of content shifting operators within the same context --what Kaplan dubbed monsters. This paper, therefore, presents an alternative account to indexical terms and sentences employing the Interactive Theory introduced in Colomina-Alminana (2022). This approach considers that the meaning of any sentence, the proposition it expresses, depends upon three interrelated factors: the speaker’s intentions when uttering, the audience’s potential uptakes of such statement, and the conventions established by the speech community both speaker and audience belong, or the linguistic interaction takes place. The critical element is the so-called speaker's point of view, an objective perspectival networking background that allows lexical and syntactic mechanisms to trigger and update potential conceptual presuppositional content shared by both speaker and audience and whose existence is prior to any context and circumstances.

Counterfactuals, causation and the asymmetry of time

Among the prominent aspects of our experience of time is its asymmetry which can be pointed at by using somewhat vague notions of fixity of the past and openness of the future. Among the influential attempts to explain this asymmetry is an analysis by David Lewis, based on his influential similarity semantic analysis of counterfactual conditionals. I examine Lewis? attempt and criticisms it was exposed to in the literature. By focusing on the criticism by Penelope Mackie, I show that Lewis? analysis can be fortified in a way consistent with his basic project so that it withstands the examined criticisms. However, one important consequence of this fortification is that its success depends on his counterfactual theory of causation. This means that every (successful) criticism of Lewis? theory of causation is a fortiori a (successful) criticism of his analysis of the relevant asymmetry of time.

Concrete Counterfactual Tests for Process Tracing: Defending an Interventionist Potential Outcomes Framework

This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from process tracing are re-expressed in terms of so-called hypothetical interventions, and concrete evidential tests are proposed which are shown to corroborate process tracing claims. In particular, three steps are prescribed for an interventionist investigation, and each step in turn is shown to make the causal analysis more robust, amongst others by disambiguating causal claims and clarifying or strengthening the existing methodological advice on counterfactual analysis. The article's claims are then illustrated using a concrete example, Haggard and Kaufman's analysis of the Argentinian transition to democracy. It is shown that interventionism could have strengthened the authors’ conclusions. The article concludes with a short Bayesian analysis of its key methodological proposals.

Causal counterfactuals without miracles or backtracking

AbstractIf the laws are deterministic, then standard theories of counterfactuals are forced to reject at least one of the following conditionals: 1) had you chosen differently, there would not have been a violation of the laws of nature; and 2) had you chosen differently, the initial conditions of the universe would not have been different. On the relevant readings—where we hold fixed factors causally independent of your choice—both of these conditionals appear true. And rejecting either one leads to trouble for philosophical theories which rely upon counterfactual conditionals—like, for instance, causal decision theory. Here, I outline a semantics for counterfactual conditionals which allows us to accept both (1) and (2). And I discuss how this semantics deals with objections to causal decision theory from Arif Ahmed.

Explanatory Asymmetry in Non-Causal Explanation

The problem of explanatory asymmetry remains a serious challenge for non-causal accounts of explanation. This paper proposes a novel solution, and it does so by appealing to the theoretical context in which an explanation is offered. The paper develops the problem of explanatory asymmetry for non-causal dependency accounts of explanation, focusing specifically on Alexander Reutlinger’s Counterfactual Theory of Explanation and recent work by Marc Lange and Lina Jansson. It defends the idea that nomological possibility with respect to a global theory is the right constraint on explanation, and it shows how this breaks the apparent symmetry in counterfactual dependence that is the source of the problem. This solution appeals to theoretical context, and the paper develops and defends the version of contextualism in explanation that is required.

Thinking Structurally: A Cognitive Framework for Understanding How People Attribute Inequality to Structural Causes

To make accurate causal inferences about social-group inequalities, people must consider structural causes. Structural causes are a distinct type of extrinsic cause—they are stable, interconnected societal forces that systematically advantage some social groups and disadvantage others. We propose a new cognitive framework to specify how people attribute inequality to structural causes. This framework is rooted in counterfactual theories of causal judgment and suggests that people will recognize structural factors as causal when they are perceived as “difference-making” for inequality above and beyond any intrinsic causes. Building on this foundation, our framework makes the following contributions. First, we propose specific types of evidence that support difference-making inferences about structural factors: within-group change (i.e., observing that disadvantaged groups’ outcomes improve under better societal conditions) and well-matched between-group comparisons (i.e., observing that advantaged group members, who have similar baseline traits to the disadvantaged group, experience more favorable societal conditions and life outcomes). Second, we consider contextual, cognitive, and motivational barriers that may complicate the availability and acceptance of this evidence. We conclude by exploring how the framework might be applied in future research examining people’s causal inferences about inequality.

An empirical Bayes approach to quantifying the impact of transportation network companies (TNCs) operations on travel demand

Assessing the impacts of new and disruptive technologies on the transportation system is crucial for planners and policymakers. This study offers an innovative method for estimating the impact of transportation network company (TNC, e.g., Uber and Lyft) operations on travel demand. Among various measures of travel demand, vehicle‐miles traveled (VMT) is tested due to its broad applications in evaluating changes in transportation policy. The evaluation is based on counterfactual theory, which compares VMT estimates after the TNCs introduction to a region to what the VMT would have been without the TNCs. The latter of the two is a counterfactual, and this study develops and demonstrates the empirical Bayes (EB) approach for obtaining counterfactual VMT estimates. The EB approach is widely used to estimate crash frequency after a particular traffic safety treatment is applied to a roadway location. We reinterpret the traffic safety treatment as being akin to the introduction of TNCs in the Atlanta region and the estimation of crash frequency as analogous to the resulting change in VMT. Since the crash experience of a roadway site may be affected by several factors, like the VMT of a region, a robust counterfactual estimate is necessary for conducting a before-after study. A counterfactual VMT estimate is obtained by combining two VMT estimates from 1) the cross‐sectional analysis for Atlanta and its regional peers and 2) the time-series analysis based on the longitudinal trend from the Atlanta region. We measure the difference between the counterfactual VMT estimate and the reported VMT estimate as an indicator of TNC impact. We find that the VMT estimates in a counterfactual scenario without TNCs are lower than the actual VMT estimates between 2012 and 2018. We estimate that the TNCs may be accounting for extra 0.6 percent average annual growth in VMT. The findings may support future TNC-related planning and policymaking. We expect the approach to be useful in estimating the effects of other disruptions, such as connected and autonomous vehicles (CAVs) introduction and lasting impact of the pandemic, on VMT.

Why Causation is Biconditional but not Proportional

Abstract In this article, I defend a biconditional counterfactual account of causation, which places equal emphasis on what I call “the presence condition” and “the absence condition,” whereas Lewis's classical counterfactual theory focuses only on the absence condition. I attempt to show that biconditionalism provides a promising treatment of supervenient causation, namely, causal cases involving the supervenience relationship. Although some philosophers confuse this account with the proportionality constraint on causation, I argue that biconditionalism is distinct from and superior to proportionalism in accommodating our reliable causal intuitions.

Double Prevention, Causal Judgments, and Counterfactuals.

Mike accidentally knocked against a bottle. Seeing that the bottle was about to fall, Jack was just about to catch it when Peter accidentally knocked against him, making Jack unable to catch it. Jack did not grab the bottle, and it fell to the ground and spilled. In double-prevention cases like these, philosophers and nonphilosophers alike tend to judge that Mike knocking into the bottle caused the beer to spill and that Peter knocking into Jack did not cause the beer to spill. This difference in causal judgment is a difficult puzzle for counterfactual theories of causal judgment; if each event had not happened, the outcome would not have, yet there is a difference in people's causal judgments. In four experiments and three supplemental experiments, we confirm this difference in causal judgments. We also show that differences in people's counterfactual thinking can explain this difference in their causal judgments and that recent counterfactual models of causal judgment can account for these patterns. We discuss these results in relation to work on counterfactual thinking and causal modeling.