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1. An empirical study of counterfactual visualization to support visual causal inference

   https://doi.org/10.1177/14738716241229437  

   Wang, Arran Zeyu; Borland, David; Gotz, David (April 2024, Information Visualization)

   Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users’ understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users’ understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants’ interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations. 

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2. Inventories, Integration, Productivity, and Welfare

   https://doi.org/10.1257/pandp.20251088  

   Alessandria, George; Khan, Shafaat Yar; Khederlarian, Armen (May 2025, AEA Papers and Proceedings)

   We augment a heterogeneous firm trade model with rich logistic frictions--order costs and delivery times--that differ by source and tariffs to evaluate how trade influences the aggregate economy. These frictions lead importers to order larger amounts less frequently, resulting in an importer inventory premium. As trade barriers fall, firms import more frequently, leading to lower inventories and more efficient distribution, releasing resources for consumption. With inventories, the gains from trade are larger. However, these long-run differences are small compared to the transition around a possible tariff increase, as firms stockpile in advance of a possible tariff increase. 

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3. The Environmental Bias of Trade Policy*

   https://doi.org/10.1093/qje/qjaa042  

   Shapiro, Joseph S (December 2020, The Quarterly Journal of Economics)

   Abstract This article describes a new fact, then analyzes its causes and consequences: in most countries, import tariffs and nontariff barriers are substantially lower on dirty than on clean industries, where an industry’s “dirtiness” is defined as its carbon dioxide (CO2) emissions per dollar of output. This difference in trade policy creates a global implicit subsidy to CO2 emissions in internationally traded goods and contributes to climate change. This global implicit subsidy to CO2 emissions totals several hundred billion dollars annually. The greater protection of downstream industries, which are relatively clean, substantially accounts for this pattern. The downstream pattern can be explained by theories where industries lobby for low tariffs on their inputs but final consumers are poorly organized. A quantitative general equilibrium model suggests that if countries applied similar trade policies to clean and dirty goods, global CO2 emissions would decrease and global real income would change little. 

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4. What Can Time‐Series Regressions Tell Us About Policy Counterfactuals?

   https://doi.org/10.3982/ECTA21045  

   McKay, Alisdair; Wolf, Christian K (September 2023, Econometrica)

   We show that, in a general family of linearized structural macroeconomic models, knowledge of the empirically estimable causal effects of contemporaneous and news shocks to the prevailing policy rule is sufficient to construct counterfactuals under alternative policy rules. If the researcher is willing to postulate a loss function, our results furthermore allow her to recover an optimal policy rule for that loss. Under our assumptions, the derived counterfactuals and optimal policies are robust to the Lucas critique. We then discuss strategies for applying these insights when only a limited amount of empirical causal evidence on policy shock transmission is available. 

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5. Explaining machine learning classifiers through diverse counterfactual explanations

   https://doi.org/10.1145/3351095.3372850  

   Mothilal, Ramaravind K.; Sharma, Amit; Tan, Chenhao (January 2020, FAT*)

   Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that effective counterfactual explanations should satisfy two properties: feasibility of the counterfactual actions given user context and constraints, and diversity among the counterfactuals presented. To this end, we propose a framework for generating and evaluating a diverse set of counterfactual explanations based on determinantal point processes. To evaluate the actionability of counterfactuals, we provide metrics that enable comparison of counterfactual-based methods to other local explanation methods. We further address necessary tradeoffs and point to causal implications in optimizing for counterfactuals. Our experiments on four real-world datasets show that our framework can generate a set of counterfactuals that are diverse and well approximate local decision boundaries, outperforming prior approaches to generating diverse counterfactuals. We provide an implementation of the framework at https://github.com/microsoft/DiCE. 

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