In the human sciences, experimental research is used to establish causal relationships. However, the extrapolation of these results to the target population can be problematic. To facilitate extrapolation, we propose to use the statistical technique Latent Class Regression Analysis in combination with the analogical reasoning theory for extrapolation. This statistical technique can identify latent classes that differ in the effect of X on Y. In order to extrapolate by means of analogical reasoning, one can characterize the latent classes by a (...) combination of features, and then compare these features to features of the target. (shrink)

The explanatory/pragmatic-trial distinction enjoys a burgeoning philosophical and medical literature and a significant contingent of support among philosophers and healthcare stakeholders as an important way to assess the design and results of randomized controlled trials. A major motivation has been the need to provide relevant, generalizable data to drive healthcare decisions. While talk of pragmatic and explanatory trials could be seen as convenient shorthand, the distinction can also be seen as harboring deeper issues related to inferential strategies used to evaluate (...) causal claims regarding medical treatments. A comprehensive, critical analysis of the distinction and underlying epistemological framework upon which the distinction is based, particularly with respect to treatment effectiveness, has yet to be forthcoming. I provide this, analyzing the distinction’s relationship to generalizability and cognate distinctions between ideal conditions and real-world practice, internal and external validity, and efficacy and effectiveness. I also analyze recent philosophical work that relies on the explanatory/pragmatic-trial distinction and that advocates for more pragmatic trials. I conclude that as an organizing principle for trial-design decisions and trial evaluation, the explanatory/pragmatic-trial distinction is conceptually problematic and not as useful as its proponents seem to think. Since some pragmatic-trial features can be inimical to establishing treatment effectiveness, pragmatic-trial features should not be conflated with pragmatic trials’ avowed goals. If the distinction is to be useful, it and some associated concepts, including generalizability, should be reformulated, lest they continue to underlie a medical epistemology that could contribute to methodologically flawed and potentially unethical advice for the design and interpretation of trials. (shrink)

Amalgamating evidence from heterogeneous sources and across levels of inquiry is becoming increasingly important in many pure and applied sciences. This special issue provides a forum for researchers from diverse scientific and philosophical perspectives to discuss evidence amalgamation, its methodologies, its history, its pitfalls, and its potential. We situate the contributions therein within six themes from the broad literature on this subject: the variety-of-evidence thesis, the philosophy of meta-analysis, the role of robustness/sensitivity analysis for evidence amalgamation, its bearing on questions (...) of extrapolation and external validity of experiments, its connection with theory development, and its interface with causal inference, especially regarding causal theories of cancer. (shrink)

While the history and methodology of laboratory experiments in economics have been extensively studied by philosophers, those of field experiments have not attracted much attention until recently. What is the historical context in which field experiments have been advocated? And what are the methodological rationales for conducting experiments in the field as opposed to in the lab? This article addresses these questions by combining historical and methodological perspectives. In terms of history, we show that the movement toward field experiments in (...) economics has two distinct roots. One is the general orientation of medical and social sciences to evidence-based policy evaluation, which gave rise to randomized field experiments in economics. The other is an awareness of several methodological limitations of lab experiments in economics, which required practitioners to get out of the lab and into the field. In these senses, the movement is a consequence of influences from both outside and inside economics: the general evidence-based trend in policy science and an internal methodological development of experimental economics. In terms of methodology, we show that these two roots resulted in two somewhat different notions of “external validity” as methodological rationales of field experiment. Finally, we suggest that analysis of experiments as exhibits highlights a methodological strategy in which both strands complement each other. (shrink)

I envision new directions in the methodology of experimental games in the field of developmental, environmental and resource economics. Although there have been extensive discussions on experimenta...

The existing philosophical views on what is the meaning of causality adequate to medicine are vastly divided. We approach this question and offer two arguments in favor of pluralism regarding concepts of causality. First, we analyze the three main types of research designs (randomized-controlled trials, observational epidemiology and laboratory research). We argue, using examples, that they allow for making causal conclusions that are best understood differently in each case (in agreement with a version of manipulationist, probabilistic and mechanistic definitions, respectively). (...) Second, we analyze clinical practice and argue that these manipulationist, probabilistic and mechanistic causal claims can be used as evidence for different therapeutic decisions. We differentiate among ‘predicting’ that does not change the relata of causal claims, (mechanistic) ‘interferences’, and ‘interventions’ in the strict sense that act on causes to change effects. The central conclusion is that causal claims agreeing with diverse concepts of causality can deliver evidence for different types of therapeutic decisions. (shrink)

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. (shrink)

In the biomedical, behavioural and social sciences, the leading method used to estimate causal effects is commonly randomised controlled trials (RCTs) that are generally viewed as both the source and justification of the most valid evidence. In studying the foundation and theory behind RCTs, the existing literature analyses important single issues and biases in isolation that influence causal outcomes in trials (such as randomisation, statistical probabilities and placebos). The common account of biased causal inference is described in a general way (...) in terms of probabilistic imbalances between trial groups. This paper expands the common account of causal bias by distinguishing between the range of biases arising between trial groups but also within one of the groups or across the entire sample during trial design, implementation and analysis. This is done by providing concrete examples from highly influential RCT studies. In going beyond the existing RCT literature, the paper provides a broader, practice-based account of causal bias that specifies the between-group, within-group and across-group biases that affect the estimated causal results of trials – impacting both the effect size and statistical significance. Within this expanded framework, we can better identify the range of different types of biases we face in practice and address the central question about the overall validity of the RCT method and its causal claims. A study can face several smaller biases (related simultaneously to a smaller sample, smaller estimated effect, greater unblinding etc.) that generally add up to greater aggregate bias. Though difficult to measure precisely, it is important to assess and provide information in studies on how much different sources of bias, combined, can explain the estimated causal effect. The RCT method is thereby often the best we have to inform our policy decisions – and the evidence is strengthened when combined with multiple studies and other methods. Yet there is room for continually improving trials and identifying ways to reduce biases they face and to increase their overall validity. Implications are discussed. (shrink)

Extrapolating causal effects is becoming an increasingly important kind of inference in Evidence-Based Policy, development economics, and microeconometrics more generally. While several strategies have been proposed to aid with extrapolation, the existing methodological literature has left our understanding of what extrapolation consists of and what constitutes successful extrapolation underdeveloped. This paper addresses this lack in understanding by offering a novel account of successful extrapolation. Building on existing contributions pertaining to the challenges involved in extrapolation, this more nuanced and comprehensive account (...) seeks to provide tools that facilitate the scrutiny of specific extrapolative inferences and general strategies for extrapolation. Offering such resources is important especially in view of the increasing amounts of real-world decision-making in policy, development, and beyond that involve extrapolation. (shrink)

This paper argues that an important type of experiment-target inference, extrapolating causal effects, requires models to be successful. Focusing on extrapolation in Evidence-Based Policy, it is ar...

Extrapolating causal effects from experiments to novel populations is a common practice in evidence-based-policy, development economics and other social science areas. Drawing on experimental evidence of policy effectiveness, analysts aim to predict the effects of policies in new populations, which might differ importantly from experimental populations. Existing approaches made progress in articulating the sorts of similarities one needs to assume to enable such inferences. It is also recognized, however, that many of these assumptions will remain surrounded by significant uncertainty in (...) practice. Unfortunately, the existing literature says little on how analysts may articulate and manage these uncertainties. This paper aims to make progress on these issues. First, it considers several existing ideas that bear on issues of uncertainty, elaborates the challenges they face, and extracts some useful rationales. Second, it outlines a novel approach, called the support graph approach, that builds on these rationales and allows analysts to articulate and manage uncertainty in extrapolation in a systematic and unified way. (shrink)

The experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the ‘quantitative revolution’ in the nineteenth century. One of the often valued features of social science experimentation is precisely the fact that there are clear methodological rules regarding hypothesis testing that come from the methods of the natural sciences and from the methodology of RCTs in the biomedical sciences, and that allow for the adjudication among contentious causal claims. We examine (...) critically this claim and argue that some current understandings of the practices that surround social science experimentation overestimate the degree to which experiments can actually fulfil this role as “objective” adjudicators, by neglecting the importance of shared background knowledge or assumptions and of consensus regarding the validity of the constructs involved in an experiment. We take issue with the way the distinction between internal and external validity is often used to comment on the inferential import of experiments, used both among practitioners and among philosophers of science. We describe the ways in which the more common use of the internal/external distinction differs from Cook and Campbell’s original methodological project, in which construct validity and the four-fold validity typology were all important in assessing the inferential import of experiments. We argue that the current uses of the labels internal and external, as applied to experimental validity, help to encroach a simplistic view on the inferential import of experiments that, in turn, misrepresents their capacity to provide objective knowledge about the causal relations between variables. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what are (...) experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the limitations of certain (...) features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts. (shrink)

The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. This enables us (...) to unlock lessons from feminist philosophy of science scholarship that need to be considered by the ML benchmark community. Finally, we outline practical recommendations for benchmark research ethics and ethics review. (shrink)