title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ¾Ð²
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: Development of models for treatment effect evaluating based on transformers
Creators: name: ÐÐ±Ð¸Ð´Ð¸Ð½Ð°, ÐÐ»ÐµÐºÑÐ°Ð½Ð´ÑÐ°
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: 2024
Subjects: Ð¼ÐµÑ Ð°Ð½Ð¸Ð·Ð¼ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ
Ð¼Ð½Ð¾Ð³Ð¾Ð³Ð¾Ð»Ð¾Ð²Ð¾Ðµ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ðµ
Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¾ÑÐµÐ½ÐºÐ° ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ
attention mechanism
multi-head attention
fully connected neural networks
treatment effect estimation
synthetic dataset generation

description: Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼Ð° Ð¼Ð½Ð¾Ð³Ð¾Ð³Ð¾Ð»Ð¾Ð²Ð¾Ð³Ð¾ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ.Â  ÐÑÐµÐ½ÐºÐ° ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ â ÑÑÐ¾ Ð·Ð°Ð´Ð°ÑÐ° ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸, ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ° Ð¿Ð¾Ð´Ð°ÑÑÑÑ Ð½Ð° Ð²ÑÐ¾Ð´ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð² Ð²Ð¸Ð´Ðµ ÑÐ°Ð±Ð»Ð¸ÑÐ½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿ÑÐµÐ´Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð° Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÐ»Ð¾Ð²Ð½Ð¾Ð³Ð¾ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ ÑÑÑÐµÐºÑÐ° Ð²Ð¾Ð·Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ, Ð¿ÑÐ¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¸Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð±Ð¸Ð½Ð°ÑÐ½ÑÐ¹ ÑÐ¸Ð¿ Ð»ÐµÑÐµÐ½Ð¸Ñ â Ð±ÑÐ»Ð¾ Ð»ÐµÑÐµÐ½Ð¸Ðµ Ð¿ÑÐµÐ´Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¾ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÑ Ð¸Ð»Ð¸ Ð½ÐµÑ. ÐÑÐ¸ Ð²ÐµÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÑÐ³ÐµÐ½ÐµÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¸ Ð¿Ð¾Ð»ÑÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð½Ð°Ð±Ð¾ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð½Ð°Ð¿ÑÐ¸Ð¼ÐµÑ, IHDP. ÐÐ°ÑÐ°ÑÐµÑÑ Ð±ÑÐ»Ð¸ ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ñ Ð½Ð° Ð¸ÑÐ¿ÑÑÑÐµÐ¼ÑÑ Ð¸ ÐºÐ¾Ð½ÑÑÐ¾Ð»ÑÐ½ÑÑ Ð³ÑÑÐ¿Ð¿Ñ Ñ ÑÐ¾Ð±Ð»ÑÐ´ÐµÐ½Ð¸ÐµÐ¼ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½ÑÑ Ð¿ÑÐ¾Ð¿Ð¾ÑÑÐ¸Ð¹. ÐÐ¾Ð´ÐµÐ»Ñ, Ð¿ÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð½Ð° ÑÑÐ¸Ñ Ð½Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð°Ð½Ð½ÑÑ, Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° Ð²ÑÑÐ¾ÐºÐ¾Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿ÑÐ¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸. Ð¢Ð°ÐºÐ¶Ðµ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿ÑÐ¸ÑÐ¸Ð½Ð½Ð¾-ÑÐ»ÐµÐ´ÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ â Ð½ÐµÐ±Ð¾Ð»ÑÑÐ¸Ñ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð±ÑÐ»Ð¸ ÑÐ³ÐµÐ½ÐµÑÐ¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð´Ð°ÑÐ°ÑÐµÑÑ. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿Ð¾ÑÐ»Ðµ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° ÑÐ¾ÑÐ¾ÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÐ»Ð¾Ð²Ð½Ð¾Ð³Ð¾ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ ÑÑÑÐµÐºÑÐ° Ð»ÐµÑÐµÐ½Ð¸Ñ Ð½Ð° Ð²Ð°Ð»Ð¸Ð´Ð°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð´Ð°ÑÐ°ÑÐµÑÐ°Ñ Ð¿ÑÐ¸ Ð½ÐµÐ±Ð¾Ð»ÑÑÐ¾Ð¼ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ðµ ÑÐ¿Ð¾Ñ Ð´Ð¾Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð½Ð¸Ñ Ð¸ Ð¿Ð¾ÑÐ»ÐµÐ´ÑÑÑÐµÐ¼ Ð¿ÑÐ¾ÑÐ¾Ð´Ðµ ÑÑÐ¸Ñ Ð´Ð°ÑÐ°ÑÐµÑÐ¾Ð² ÑÐµÑÐµÐ· Ð´Ð°Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ñ.
description: The paper proposes an architecture of a machine learning model with a multi-head attention mechanism to solve the problem of evaluating the personalized treatment effect. Treatment effect estimation is a regression task where patient characteristics are fed into the model as tabular data. The implemented model is designed to estimate the conditional average treatment effect (CATE), considering a binary treatment type, whether the patient received the treatment or not. The models verification utilized generated synthetic and semi-synthetic datasets, for example, IHDP. The datasets were divided into test and control groups in accordance with certain proportions. The model, pre-trained on these datasets, demonstrated high-quality estimation performance during testing. Additionally, synthetic datasets were generated using structured causal models â fully-connected networks. The proposed model, after pre-training on these datasets, demonstrated good results in estimating the conditional average treatment effect on validation datasets with a small number of fine-tuning epochs and subsequent passing of these datasets through the model.