Causal inference is often portrayed as fundamentally distinct from predictive modeling, with its own terminology, goals, and intellectual challenges. But at its core, causal inference is simply a structured instance of prediction under distribution shift. In both cases, we begin with labeled data from a source domain and seek to generalize to a target domain where outcomes are not observed. The key difference is that in causal inference, the labels -- potential outcomes -- are selectively observed based on treatment assignment, introducing bias that must be addressed through assumptions. This perspective reframes causal estimation as a familiar generalization problem and highlights how techniques from predictive modeling, such as reweighting and domain adaptation, apply directly to causal tasks. It also clarifies that causal assumptions are not uniquely strong -- they are simply more explicit. By viewing causal inference through the lens of prediction, we demystify its logic, connect it to familiar tools, and make it more accessible to practitioners and educators alike.

This paper proposes techniques to enhance the performance of non-causal models for causal inference using data from randomized experiments. In domains like advertising, customer retention, and precision medicine, non-causal models that predict outcomes under no intervention are often used to score individuals and rank them according to the expected effectiveness of an intervention (e.g, an ad, a retention incentive, a nudge). However, these scores may not perfectly correspond to intervention effects due to the inherent non-causal nature of the models. To address this limitation, we propose causal fine-tuning and effect calibration, two techniques that leverage experimental data to refine the output of non-causal models for different causal tasks, including effect estimation, effect ordering, and effect classification. They are underpinned by two key advantages. First, they can effectively integrate the predictive capabilities of general non-causal models with the requirements of a causal task in a specific context, allowing decision makers to support diverse causal applications with a "foundational" scoring model. Second, through simulations and an empirical example, we demonstrate that they can outperform the alternative of building a causal-effect model from scratch, particularly when the available experimental data is limited and the non-causal scores already capture substantial information about the relative sizes of causal effects. Overall, this research underscores the practical advantages of combining experimental data with non-causal models to support causal applications.

This paper introduces causal scoring as a novel approach to frame causal estimation in the context of decision making. Causal scoring entails the estimation of scores that support decision making by providing insights into causal effects. We present three valuable causal interpretations of these scores: effect estimation (EE), effect ordering (EO), and effect classification (EC). In the EE interpretation, the causal score represents the effect itself. The EO interpretation implies that the score can serve as a proxy for the magnitude of the effect, enabling the sorting of individuals based on their causal effects. The EC interpretation enables the classification of individuals into high- and low-effect categories using a predefined threshold. We demonstrate the value of these alternative causal interpretations (EO and EC) through two key results. First, we show that aligning the statistical modeling with the desired causal interpretation improves the accuracy of causal estimation. Second, we establish that more flexible causal interpretations are plausible in a wider range of data-generating processes and propose conditions to assess their validity. We showcase the practical utility of the causal scoring framework through examples in diverse fields such as advertising, healthcare, and education, illustrating how it facilitates reasoning about flexible causal interpretations of statistical estimates in various contexts. The examples encompass confounded estimates, effect estimates on surrogate outcomes, and even predictions about non-causal quantities as potential causal scores.

Causal decision making (CDM) at scale has become a routine part of business, and increasingly CDM is based on machine learning algorithms. For example, businesses often target offers, incentives, and recommendations with the goal of affecting consumer behavior. Recently, we have seen an acceleration of research related to CDM and to causal effect estimation (CEE) using machine learned models. This article highlights an important perspective: CDM is not the same as CEE, and counterintuitively, accurate CEE is not necessary for accurate CDM. Our experience is that this is not well understood by practitioners nor by most researchers. Technically, the estimand of interest is different, and this has important implications both for modeling and for the use of statistical models for CDM. We draw on recent research to highlight three of these implications. (1) We should carefully consider the objective function of the causal machine learning, and if possible, we should optimize for accurate "treatment assignment" rather than for accurate effect-size estimation. (2) Confounding does not have the same effect on CDM as it does on CEE. The upshot here is that for supporting CDM it may be just as good to learn with confounded data as with unconfounded data. Finally, (3) causal statistical modeling may not be necessary at all to support CDM, because there may be (and perhaps often is) a proxy target for statistical modeling that can do as well or better. This observation helps to explain at least one broad common CDM practice that seems "wrong" at first blush: the widespread use of non-causal models for targeting interventions. Our perspective is that these observations open up substantial fertile ground for future research. Whether or not you share our perspective completely, we hope we facilitate future research in this area by pointing to related articles from multiple contributing fields.

This study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and has received significant attention from economists, computer scientists, and social scientists. We characterize the various methods proposed in the literature into three general approaches: learning models to predict outcomes, learning models to predict causal effects, and learning models to predict optimal treatment assignments. We show analytically that optimizing for outcome or causal-effect prediction is not the same as optimizing for treatment assignments, and thus we should prefer learning models that optimize for treatment assignments. We then compare and contrast the three approaches empirically in the context of choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. This is the first comparison of the different treatment assignment approaches on a real-world application at scale (based on more than half a billion individual treatment assignments). Our results show (i) that applying different algorithms to different users can improve streams substantially compared to deploying the same algorithm for everyone, (ii) that personalized assignments improve substantially with larger data sets, and (iii) that learning models by optimizing treatment assignments rather than outcome or causal-effect predictions can improve treatment assignment performance by more than 28%.