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Questions that motivate most studies on the emergence and reproduction of health inequalities over the life course are longitudinal and causal in nature. Fortunately, after several decades of socio-epidemiological research that produced numerous cross-sectional (and thus associational) findings, the need for studies aiming at disentangling causal effects and mechanisms from non-causal associations is becoming increasingly recognized. Especially, attempts to estimate causal effects of social background variables (e.g., marital and family status or occupational status) and/ or life changing events (e.g., pregnancy, separation and divorce, death of a family member, retirement) on health over time – including selection and reverse causality – have to be undertaken using advanced data structures and analytical techniques suited for causal inference. Following recent developments in causal theory including directed acyclic graphs (DAG), new opportunities for innovative health research are being generated by the increasing availability of rich panel data with multiple times of observation and different types of health measures (biomarkers, fitness tracker data, results of objective medical tests, and a variety of subjective indicators). Nonetheless, causal analyses on health inequalities over time remain challenging for many reasons, and one of them is health being a strong predictor of attrition in panel studies (due to nonresponse or death). Several analytical approaches have been proposed as a remedy for the manifold challenges regarding causal inference in health research. The first one deals with health-related self-selection as a missing data problem and uses techniques for correcting causal effect estimates such as weighting or dropout models for nonignorable nonresponse (e.g., Heckman selection models). The second one focuses on reducing unobserved heterogeneity (e.g., via instrumental variables, propensity score matching, fixed-effects regression). The third one places emphasis on testing potential intervening mechanisms (mediator and suppressor effects). The fourth one corresponds with the use of agent-based modeling (e.g., with SIENA) enabling simultaneous estimation of selection and causation effects in a social network framework. Finally, the consideration of measurement error while dealing with latent variables may serve to adjust causal effect estimates for (lack of) reliability. In the light of the above challenges, we invite submissions applying any of the mentioned approaches (or other innovative analysis techniques) with the goal to improve understanding of causality and selection in (preferably longitudinal) research on health inequalities. Welcome are contributions that aim at unraveling the causal complexity between social background variables and/ or life course transitions and events, and any kind of objective or subjective health outcome.