Recursive Partitioning for Heterogeneous Causal Effects

In this paper we study the problems of estimating heterogeneity in causal effects in experimental or observational studies and conducting inference about the magnitude of the differences in treatment effects across subsets of the population. In applications, our method provides a data-driven approach to determine which subpopulations have large or small treatment effects and to test hypotheses about the differences in these effects. For experiments, our method allows researchers to identify heterogeneity in treatment effects that was not specified in a pre-analysis plan, without concern about invalidating inference due to multiple testing. In most of the literature on supervised machine learning (e.g. regression trees, random forests, LASSO, etc.), the goal is to build a model of the relationship between a unit's attributes and an observed outcome. A prominent role in these methods is played by cross-validation which compares predictions to actual outcomes in test samples, in order to select the level of complexity of the model that provides the best predictive power. Our method is closely related, but it differs in that it is tailored for predicting causal effects of a treatment rather than a unit's outcome. The challenge is that the "ground truth" for a causal effect is not observed for any individual unit: we observe the unit with the treatment, or without the treatment, but not both at the same time. Thus, it is not obvious how to use cross-validation to determine whether a causal effect has been accurately predicted. We propose several novel cross-validation criteria for this problem and demonstrate through simulations the conditions under which they perform better than standard methods for the problem of causal effects. We then apply the method to a large-scale field experiment re-ranking results on a search engine

Native vegetation of the southern forests : south-east highlands, Australian alps, south-west Slopes, and SE Corner bioregions

The Southern Forests study area covers an area of about six million hectares of south-eastern New South Wales, south of Oberon and Kiama and east of Albury and Boorowa (latitude 33° 02’–37 ° 06’ S; longitude 146° 56’ – 147° 06’ E). The total area of existing vegetation mapped was three million hectares (3 120 400 hectares) or about 50% of the study area. Terrestrial, wetland and estuarine vegetation of the Southern Forests region were classified into 206 vegetation groups and mapped at a scale between 1: 25 000 and 1: 100 000. The classification was based on a cluster analysis of detailed field surveys of vascular plants, as well as field knowledge in the absence of field survey data. The primary classification was based on 3740 vegetation samples with full floristics cover abundance data. Additional classifications of full floristics presence-absence and tree canopy data were carried out to guide mapping in areas with few full floristic samples. The mapping of extant vegetation was carried out by tagging vegetation polygons with vegetation codes, guided by expert knowledge, using field survey data classified into vegetation groups, remote sensing, and other environmental spatial data. The mapping of pre-1750 vegetation involved tagging of soils mapping with vegetation codes at 1: 100 000 scale, guided by spatial modelling of vegetation groups using generalised additive statistical models (GAMS), and expert knowledge. Profiles of each of the vegetation groups on the CD-ROM* provide key indicator species, descriptions, statistics and lists of informative plant species. The 206 vegetation groups cover the full range of natural vegetation, including rainforests, moist eucalypt forests, dry shrub forests, grassy forests, mallee low forests, heathlands, shrublands, grasslands and wetlands. There are 138 groups of Eucalyptus forests or woodlands, 12 rainforest groups, and 46 non-forest groups. Of the 206 groups, 193 were classified and mapped in the study area. Thirteen vegetation groups were not mapped because of their small size and lack of samples, or because they fell outside the study area. Updated regional extant and pre-1750 vegetation maps of southern New South Wales have been produced in 2005, based on those originally prepared in 2000 for the southern Regional Forest Agreement (RFA). Further validation and remapping of extant vegetation over 10% of the study area has subsequently improved the quality of the vegetation map, and removed some of the errors in the original version. The revised map provides a reasonable representation of native vegetation at a scale between 1: 25 000 and 1: 100 000 across the study area. In 2005 native vegetation covers 50% of the study area. Environmental pressures on the remaining vegetation include clearing, habitat degradation from weeds and nutrification, severe droughts, changing fire regimes, and urbanisation. Grassy woodlands and forests, temperate grasslands, and coastal and riparian vegetation have been the most reduced in areal extent. Over 90% of the grassy woodlands and temperate grasslands have been lost. Conservation of the remaining vegetation in these formations is problematic because of the small, discontinuous, and degraded nature of the remaining patches of vegetation

Deep Learning for User Comment Moderation

Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation