Nations Unies. World Population Prospects. The 1996 Revision. New York, Department of Economie and Social Affairs, Population Division, ST/ESA/SER.A/167, 1998, 839 pages. Nations Unies. World Population Projections to 2150. New York, Department of Economie and Social Affairs, Population Division, ST/ESA/SER.A/173, 1998, 41 pages.

This chapter reviews population trends over the last two hundred years and population projections to the end of this century. In 2100 the world population will have stabilized but its geographical distribution will have substantially changed compared to 2015. The chapter then discusses the five stages of the demographic transition, and different neo-Malthusian and non-Malthusian theories of the relation between population growth and economic development. It emphasizes in particular the effects of rapid population growth on land and resource availability, human capital formation, population quality, the accumulation of physical capital, employment, wages, and income inequality. The effects of rapid population growth rate over a given period were found to change in line with the population size and density at the beginning of the period considered.

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Recent Trends in Fertility in Industrialised Countries. (Population Studies No. 27. United Nations, Dept. of Economic and Social Affairs. New York. 1958. Pp. 182. Price 14s.)

Health Education Journal ◽

10.1177/001789695901700211 ◽

1959 ◽

Vol 17 (2) ◽

pp. 149-149

Keyword(s):

New York ◽

United Nations ◽

Population Studies ◽

Recent Trends ◽

Industrialised Countries ◽

Social Affairs

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Invariant Causal Prediction for Nonlinear Models

Journal of Causal Inference ◽

10.1515/jci-2017-0016 ◽

2018 ◽

Vol 6 (2) ◽

Cited By ~ 7

Author(s):

Christina Heinze-Deml ◽

Jonas Peters ◽

Nicolai Meinshausen

Keyword(s):

Linear Models ◽

Causal Model ◽

Nonlinear Models ◽

Causal Structure ◽

Nonparametric Tests ◽

Population Projections ◽

World Population ◽

Residual Distribution ◽

Using Data

AbstractAn important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure. To this end, Invariant Causal Prediction (ICP) [1] has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straightforward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for conditional independence.In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables. We find that an approach which first fits a nonlinear model with data pooled over all environments and then tests for differences between the residual distributions across environments is quite robust across a large variety of simulation settings. We call this procedure “invariant residual distribution test”. In general, we observe that the performance of all approaches is critically dependent on the true (unknown) causal structure and it becomes challenging to achieve high power if the parental set includes more than two variables.As a real-world example, we consider fertility rate modeling which is central to world population projections. We explore predicting the effect of hypothetical interventions using the accepted models from nonlinear ICP. The results reaffirm the previously observed central causal role of child mortality rates.

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