Confounding and control

This paper deals both with the issues of confounding and of control, as the definition of a confounding factor is far from universal and there exist different methodological approaches, ex ante and ex post, for controlling for a confounding factor. In the first section the paper compares some definitions of a confounder given in the demographic and epidemiological literature with the definition of a confounder as a common cause of both treatment/exposure and response/outcome. In the second section, the paper examines confounder control from the data collection viewpoint and recalls the stratification approach for ex post control. The paper finally raises the issue of controlling for a common cause or for intervening variables, focusing in particular on latent confounders.confounding, control, structural modelling

Causality in Econometric Modeling. From Theory to Structural Causal Modeling

This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed. A distinction is then drawn between model-based and design-based approaches. Some more recent developments are examined next, namely history-friendly simulation and information-theory based approaches. Finally, in a constructive perspective, structural causal modeling (SCM) is presented, based on the concepts of mechanism and sub-mechanisms, and of recursive decomposition of the joint distribution of variables. This modeling strategy endeavors at representing the structure of the underlying data generating process. It operationalizes the concept of causation through the ordering and role-function of the variables in each of the intelligible sub-mechanisms

Time and Causality in the Social Sciences

This article deals with the role of time in causal models in the social sciences, in particular in structural causal modeling, in contrast to time-free models. The aim is to underline the importance of time-sensitive causal models. For this purpose, it also refers to the important discussion on time and causality in the philosophy of science, and examines how time is taken into account in demography and in economics as examples of social sciences. Temporal information is useful to the extent that it is placed in a correct causal structure, and thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant for explanatory purposes than the temporal order, the former should nevertheless take into account the time-patterns of causes and effects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models

Demografia y Sociologia

Incluye Bibliografí

Direct and indirect paths leading to contraceptive use in urban Africa

Résumé Cet article examine le recours à la contraception dans les capitales de quatre pays africains, le Burkina Faso, le Ghana, le Maroc et le Sénégal. L’article cherche à répondre à deux questions : (i) quel est l’ordre hiérarchique des relations causales entre les caractéristiques individuelles associées au recours à la contraception dans les quatre populations urbaines considérées ? Plus particulièrement, (ii) comme l’instruction est un facteur majeur de la transition démographique, les données confirment-elles les deux chemins indirects allant de l’instruction au recours à la contraception qui ont été proposés dans la littérature, à savoir un chemin union-re­production et un chemin socio-culturel ? À partir d’une analyse secondaire des En­quêtes Démographie et Santé (EDS), la méthodologie se base sur des modèles structurels récursifs représentés par des graphes acycliques orientés. L’analyse em­pirique confirme l’importance de variables telles que le désir d’enfants et l’accord parental en matière de planification familiale pour expliquer le recours à la contraception. L’analyse met aussi en relief un chemin structurel union-reproduction as­sociant instruction féminine et recours à la contraception. En revanche, l’analyse aboutit à rejeter l’existence d’un chemin socioculturel, celui-ci étant infirmé par les données disponibles. La validité de ces résultats est discutée. Abstract This study examined contraceptive use in the capital cities of four African countries, Burkina Faso, Ghana, Morocco and Senegal. The article sought to answer two questions: (i) what is the hierarchical ordering of causal relationships among the individual factors involved in the use of contraception in the four urban populations considered? More particularly, (ii) as education is a major factor of fertility transition, are two main indirect pathways that have been proposed in the literature (a union-reproductive path and a socio-cultural one), leading from women’s education to contraceptive use, confirmed by the data? Having recourse to a secondary analysis of Demographic and Health Survey(DHS) data, the methodology is based on recursive structural models represented by directed acyclic graphs. The empirical analysis confirms the importance of variables such as the desire for children and partner agreement on family planning in explaining contraceptive use. It also highlights a structural union-reproductive path linking female education and contraceptive use. On the contrary, the analysis leads to a tentative rejection of the socio-cul­tural path, as it is falsified by the data available. The validity of these results is discussed

Big Data, Demography, and Causality

The objectives of this paper are to examine to what extent Big Data are presently used in population research and to consider their potential for causal inference. After examining the characteristics and challenges of big data, the subsequent section deals with the use of big data in the study of the key demographic phenomena and is based on a literature review for the period 2015-2022 of 63 scientific journals concerned with population issues. The final section examines to what extent the use of big data could improve causal inference. Our results show that demographers continue to privilege sources of numerical data and are less prone to use digital media data or other sources such as images. Big Data can contribute to improving explanations in demography thanks to the large number of observations and variables in the data sets, especially when they can be individually linked together. Causal knowledge requires however that one can propose and test a suitable mechanism explaining why a variation in one variable produces a variation in another variable

The issue of control in multivariate systems, A contribution of structural modelling.

This paper builds upon Judea Pearl’s directed acyclic graphs approach to causality and the tradition of structural modelling in economics and social science. The paper re-examines the issue of control in complex systems with multiple causes and outcomes, in a specific perspective of structural modelling. It begins with three-variable saturated and unsaturated models, and then examines more complex systems including models with collider and latent confounder discussed by Pearl. In particular, focusing on the causes of an outcome, the paper proposes two simple rules for selecting the variables to be controlled for when studying the direct effect of a cause on an outcome of interest or the total effect when dealing with multiple causal paths. This paper presents a model building strategy that allows a statistical model to be considered as structural. The challenge for the model builder amounts to developing an explanation through a recursive decomposition of the joint distribution of the variables congruent with background knowledge and stable with respect to specified changes of the environment

Karst spring discharge modeling based on deep learning using spatially distributed input data

Despite many existing approaches, modeling karst water resources remains challenging as conventional approaches usually heavily rely on distinct system knowledge. Artificial neural networks (ANNs), however, require only little prior knowledge to automatically establish an input–output relationship. For ANN modeling in karst, the temporal and spatial data availability is often an important constraint, as usually no or few climate stations are located within or near karst spring catchments. Hence, spatial coverage is often not satisfactory and can result in substantial uncertainties about the true conditions in the catchment, leading to lower model performance. To overcome these problems, we apply convolutional neural networks (CNNs) to simulate karst spring discharge and to directly learn from spatially distributed climate input data (combined 2D–1D CNNs). We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorological–hydrological characteristics and hydrodynamic system properties. We compare the proposed approach both to existing modeling studies in these regions and to our own 1D CNN models that are conventionally trained with climate station input data. Our results show that all the models are excellently suited to modeling karst spring discharge (NSE: 0.73–0.87, KGE: 0.63–0.86) and can compete with the simulation results of existing approaches in the respective areas. The 2D models show a better fit than the 1D models in two of three cases and automatically learn to focus on the relevant areas of the input domain. By performing a spatial input sensitivity analysis, we can further show their usefulness in localizing the position of karst catchments

When best is the enemy of good – critical evaluation of performance criteria in hydrological models

Performance criteria play a key role in the calibration and evaluation of hydrological models and have been extensively developed and studied, but some of the most used criteria still have unknown pitfalls. This study set out to examine counterbalancing errors, which are inherent to the Kling–Gupta efficiency (KGE) and its variants. A total of nine performance criteria – including the KGE and its variants, as well as the Nash–Sutcliffe efficiency (NSE) and the modified index of agreement (d1) – were analysed using synthetic time series and a real case study. Results showed that, when assessing a simulation, the score of the KGE and some of its variants can be increased by concurrent overestimation and underestimation of discharge. These counterbalancing errors may favour bias and variability parameters, therefore preserving an overall high score of the performance criteria. As bias and variability parameters generally account for two-thirds of the weight in the equation of performance criteria such as the KGE, this can lead to an overall higher criterion score without being associated with an increase in model relevance. We recommend using (i) performance criteria that are not or less prone to counterbalancing errors (d1, modified KGE, non-parametric KGE, diagnostic efficiency) and/or (ii) scaling factors in the equation to reduce the influence of relative parameters