title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾Ð¼Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¸ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ ÐÐ°Ð´Ð°ÑÐ°Ñ-Ð£Ð¾ÑÑÐ¾Ð½Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Developing and researching a treatment effect estimation model based on the composition of a convolutional network and Nadaraya-Watson regression
Creators: name: ÐÑÑÐ°Ð¼ÐµÐ´ÑÐ¸Ð½, ÐÐ³Ð¾ÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÑÐµÐ³ÑÐµÑÑÐ¸Ñ ÐÐ°Ð´Ð°ÑÐ°Ñ-Ð£Ð¾ÑÑÐ¾Ð½Ð°
Ð¼Ð¾Ð´ÐµÐ»Ñ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¾ÑÐµÐ½ÐºÐ° ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Nadaraya-Watson regression
attention model
convolutional neural networks
treatment effect estimation
image processing

description: Ð Ð°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ Ð²ÐµÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸ÐµÐ¼ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÑÐ´ÑÐ°, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð² Ð²Ð¸Ð´Ðµ Ð¾Ð±ÑÑÐ°ÐµÐ¼Ð¾Ð¹ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð² ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ ÐÐ°Ð´Ð°ÑÐ°Ñ-Ð£Ð¾ÑÑÐ¾Ð½Ð° Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ Ð¿ÑÑÐµÐ¼ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð±Ð¾Ð»ÑÑÐ¾Ð¹ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐ¼Ð¸ ÑÐµÑÑÐ¼Ð¸. Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð° Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐ¹ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÐ»Ð¾Ð²Ð½Ð¾Ð³Ð¾ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ Ñ Ð±Ð¸Ð½Ð°ÑÐ½ÑÐ¼ ÑÐ¸Ð¿Ð¾Ð¼ Ð»ÐµÑÐµÐ½Ð¸Ñ Ð¸ Ð²ÑÐ¾Ð´Ð½ÑÐ¼Ð¸ Ð´Ð°Ð½Ð½ÑÐ¼Ð¸, Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÑÑÑÐ¸Ð¼Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð±Ð¾Ð»ÑÑÐ¾Ð¹ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸, Ð² ÑÑÐ»Ð¾Ð²Ð¸ÑÑ Ð´Ð¸ÑÐ±Ð°Ð»Ð°Ð½ÑÐ° ÐºÐ¾Ð½ÑÑÐ¾Ð»ÑÐ½Ð¾Ð¹ Ð¸ ÑÐµÑÑÐ¾Ð²Ð¾Ð¹ Ð¿Ð¾Ð´Ð³ÑÑÐ¿Ð¿. ÐÐ»Ñ Ð·Ð°Ð´Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸, ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ñ Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ²Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, ÑÐµÐ°Ð»Ð¸Ð·ÑÑÑÐµÐ¹ ÑÑÐ½ÐºÑÐ¸Ñ ÑÐ´ÑÐ° Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð² Ð²Ð¸Ð´Ðµ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ ÐÐ°Ð´Ð°ÑÐ°Ñ-Ð£Ð¾ÑÑÐ¾Ð½Ð°, Ð½Ð°Ð´ ÑÐ°ÑÑÐ¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÐ¼ Ð½ÐµÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ¸Ð¼ Ð³Ð°ÑÑÑÐ¾Ð²ÑÐ¼ ÑÐ´ÑÐ¾Ð¼.Â  ÐÐ»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð² Ð·Ð°Ð´Ð°ÑÐ°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð¾Ð±ÑÐµÐ´Ð¾ÑÑÑÐ¿Ð½ÑÐµ Ð½Ð°Ð±Ð¾ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ CIFAR-10, FGVC-Aircraft, Ð° Ð´Ð»Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½ Ð½Ð°Ð±Ð¾Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ UTKFace.
description: The paper is dedicated to the implementation and verification of a machine learning model with an attention model representation via a kernel, realized as a trainable fully-connected neural network, in Nadaraya-Watson regression for solving classification and regression tasks by processing high-dimensional images with convolutional networks. The implemented machine learning model has been applied to solve the general task of estimating the conditional average treatment effect with a binary type of treatment and input data represented by high-dimensional images under conditions of imbalance of the control and treatment groups. The benefits of using a fully-connected neural network, implementing a kernel function in the attention model in the form of Nadaraya-Watson regression, over the frequently used non-parametric Gaussian kernel, have been shown for regression, classification, and treatment effect estimation tasks. Publicly available image sets CIFAR-10 and FGVC-Aircraft were used for training models in classification tasks, while the UTKFace image set was used for regression and treatment effect estimation.