Empirical research on construals—social affinity groups that share similar patterns of meaning— has advanced significantly in recent years. This progress is largely driven by the development of Construal Clustering Methods (CCMs), which group survey respondents into construal clusters based on similarities in their response patterns. We identify key limitations of existing CCMs, which affect their accuracy when applied to the typical structures of available data, and introduce Bipolar Class Analysis (BCA), a CCM designed to address these shortcomings. BCA measures similarity in response shifts between expressions of support and rejection across survey respondents, addressing conceptual and measurement challenges in existing methods. We formally define BCA and demonstrate its advantages through extensive simulation analyses, where it consistently outperforms existing CCMs in accurately identifying construals. Along the way, we develop a novel data-generation process that approximates more closely how individuals map latent opinions onto observable survey responses, as well as a new metric to evaluate the performance of CCMs. Additionally, we find that applying BCA to previously studied real-world datasets reveals substantively different construal patterns compared to those generated by existing CCMs in prior empirical analyses. Finally, we discuss limitations of BCA and outline directions for future research.

Predicting the risk of death for chronic patients is highly valuable for informed medical decision-making. This paper proposes a general framework for dynamic prediction of the risk of death of a patient given her hospitalization history. Predictions are based on a joint model for the death and hospitalization processes, thereby avoiding the potential bias arising from selection of survivors. The framework is valid for arbitrary models for the hospitalization process—it does not require independence of hospitalization times nor gap times. In particular, we study the prediction of the risk of death in a renewal model for hospitalizations, a common approach to recurrent event modeling. In the renewal model, the distribution of hospitalizations throughout the follow-up period impacts the risk of death. This result differs from the prediction of death when considering the Poisson model for the hospitalization process, previously studied, where only the number of hospitalizations matters. We apply our methodology to a prospective, observational cohort study of 512 patients treated for chronic obstructive pulmonary disease in one of six outpatient respiratory clinics run by the Respiratory Service of Galdakao University Hospital, with a median follow-up of 4.7 years. We find that more concentrated hospitalizations increase the risk of death and that the hazard ratio for death continuously increases as the number of hospitalizations increases during follow-up.

This paper investigates the differences in female work experience across Central and Eastern European countries (CEECs). We use retrospective SHARELIFE data to analyse women's work history from 1950 to 1990. We provide descriptive evidence that women's work experience varied across CEECs. Furthermore, we argue that comparing the former provinces of the Russian Empire in Lithuania and Poland provides a natural experiment, allowing us to disentangle the effect of the differential implementation of the Soviet regime from the pre-existing differences. We find that during communism, Lithuanian women worked 2 years more by age 50 relative to their Polish counterparts. This effect is one-third of that found in the East-West Germany comparison. We propose several potential mechanisms behind this finding: the degree of land collectivization, the Church's influence and the sectoral composition. Accordingly, this study's findings highlight the importance of country differences in CEECs.

Machine-learning (ML) methods now routinely generate regressors used in subsequent econometric analyses, for example, estimated propensity scores, control-function residuals, imputed covariates, learned proxies, or low-dimensional embeddings of high-dimensional data. As these ML-generated regressors become ubiquitous, the lack of general inference methods for models that use them has become a critical limitation. Standard plug-in and Double ML procedures ignore how generated regressors enter later stages, leading to large biases and invalid inference. We develop a three-step locally robust GMM framework for inference with ML generated regressors. A key new insight is downstream local robustness: by a functional chain rule, moment functions that are constructed to be orthogonal to the second step eliminate the complicated indirect (conditioning) effects from the ML-generated regressors. We show how to implement this automatically by estimating the associated Riesz representers through cross-fitted auxiliary regressions, allowing for generic non-Donsker ML in both early steps. In leading treatment-effect and counterfactual settings, simulations demonstrate severe bias in existing methods and reductions of 85-95% using our procedures.

This paper studies semiparametric Fisher information in models parametrized by general normed spaces. The main contribution is to establish that positive semiparametric Fisher information is equivalent to the gradient of the parameter of interest lying in the range of the adjoint score operator. This result generalizes a key theorem Van Der Vaart (1991) and provides a unified framework linking differentiability and information, beyond Hilbert spaces. The paper develops a normed-space mean-square-differentiable models for two canonical problems: estimation of the average of a known transformation and estimation of a density at a point. In these applications, it shows that positive information holds if and only if the transformation has finite variance and if and only if the density has positive mass at the evaluation point, respectively. These findings offer a novel information-theoretic perspective on known minimax results and clarify the conditions under which root-n estimation is possible.

The Counterfactual Average Structural Function (CASF) is the Average Structural Function (ASF) averaged with respect to a counterfactual distribution of covariates. In the binary choice model, the CASF measures the rate of successes in a counterfactual scenario. This paper shows that the CASF is non-parametrically identified as a weighted average, where the weights are given by a likelihood ratio. Estimation of the CASF at the regular root n-rate requires a square integrability condition for those weights. This necessary condition depends on instrument strength, the degree of endogeneity, and the relevance of the regressors. Much insight is gained from the single normal regressor model. In this setting, I show that extrapolation strength (the capacity of the model to regularly identify the CASF) increases with the relevance of the regressor and, surprisingly, with the degree of endogeneity. The impact of instrument strength is found to be non-monotone. Moreover, I find a threshold for instrument strength below which regular identification of the CASF is not possible.