Dear colleagues, We invite submissions to our session on "Methods for Improving Causal Inference in the Social Sciences" at the 6th Conference of the European Survey Research Association (ESRA) in Reykjavik, Iceland, 13-17 July 2015. Summary: The central aim of social sciences is to discover the causal effect of explanatory variables on various outcomes. However, in the case of most research questions we are restricted to quasi-experimental, observational and in particular cross sectional survey data. Under these conditions, the causal status of observed associations remains unclear, because of unobserved heterogeneity and reverse causation. At present the methodological tools for improving causal inference are not common knowledge. These tools are firstly different kinds of fixed-effects estimation (e.g. family or school fixed-effects) which exploits the within-variance of the respective units, eliminating the possibly endogenous between-variance. When longitudinal panel data is available, different methods like individual fixed effects panel regression, cross-lagged autoregressive models, latent growth curve models, and autoregressive latent trajectory models are used to decide on causality. Differences-in-differences estimators can be used on the aggregate level. Secondly, matching-techniques (e.g. propensity score matching) comparing the difference of similar members of the treatment and control group are feasible as well. Thirdly, instrumental variable approaches, utilizing exogenous determinants of the treatment condition to estimate causal effects. Fourthly, regression discontinuity techniques are used in order to exploit variance at the edge between strata of the explanatory variables. All these methods of causal analysis make certain assumptions, for instance no systematic missing data, the SUTVA-condition and unconfoundedness. In contrast to more common methods of analysis, the consequences of violations of these assumptions are much less analyzed. The same is true for decisions which have to be made when causal analyses are applied. For instance, which criterion should be used in the case of matching-techniques? How to judge the exogeneity and strength of an instrumental variable? This session invites methodological and empirical contributions which apply methods for improving causal inference in survey research, compares them to ��naive�� methods or presents progress in methodological issues. Proposals must be submitted online by 15 January 2015 via the conference webpage: <http://www.europeansurveyresearch.org/conference> http://www.europeansurveyresearch.org/conference If you have any questions, please do not hesitate to contact us: Jochen Mayerl, TU Kaiserslautern, Germany, Jochen.Mayerl@sowi.uni-kl.de Volker Stock��, University of Kassel, Germany, <mailto:volker.stocke@uni-kassel.de> volker.stocke@uni-kassel.de Levente Littvay, Central European University, Hungary, littvayl@ceu�\ budapest.edu