In this paper, I present a simple characterization of the sample selection bias problem that is also applicable to the conceptually distinct econometric problems that ...

Biased samples can lead to inaccurate and misleading results in machine learning, which can arise from various sources, such as the way you collect, select, or preprocess your data. To avoid ...

Despite high cost effectiveness for marketing research, such endogenously selected samples must be carefully analyzed to avoid selection bias. We introduce a unified and efficient approach based on ...

Sample bias can distort the results of your marketing ... the criteria that are relevant for your marketing decision. For example, if you want to test the demand for a new product in Europe ...

Draw plot for bias results and group samples by user’s specification Some example data from GSE112522 are deposited in folder 'example_data' for testing. Annotation files are deposited in folder ...

Aim: Spatial sampling bias (SSB) is a feature of opportunistically sampled species records. Species distribution models (SDMs) built using these data (i.e. presence-background models) can produce ...

use "https://github.com/tomzylkin/ppml_fe_bias/blob/master/examples/PPMLFEBIAS_EXAMPLE_DATA.dta?raw=true" if category=="MANUF", clear // can change category to ...

Abstract: We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first ...