Causal Analysis Experiment of Academic Paper's Title length on Number of Citation

This is for Econometrics and other Economics related materials

Causal inference between minimum wage and employment using difference in differences

Causal analysis and inference using observational and interventional dataset. It contains tools for graph structure recovery.

Survey design, causality study and statistical analysis

Notes about Statistical Rethinking | Richard McElreath

projet de causalité

Starter Pack About Mendelian Randomization. My Feedback about MR traning followed in 2020.

Less than 4% of women believe they are beautiful, according to a 2011 Dove study. Beauty standards are set by unrealistic portrayals of women in the media. We are designing an experiment to determine if changing the models shown in an advertisement will cause a change in how beauty is perceived.

This project analyses whether people who have attained job trainings makes more money than those who have not attained these trainings.

CausalVerse: An R toolkit expediting causal research & analysis. Streamlines complex methodologies, empowering users to unveil causal relationships with precision. Your go-to for insightful causality exploration.

R Code and reporting output for randomized causal inference study to answer question on whether there is bias in following directions from gendered voices.

Conducting one-factor analysis of variance and its assumptions (normality and homogeneity of variance) with R if two groups of data show statistically different behavior. Visualizing data with boxplots to understand if the given state of the fly (fed/starved) affects the feeding and resting pattern.

Granger causality of the inflation–growth mirror in accession countries, (ET, 2004)

Repositorio para el proyecto final de la clase Métodos Analíticos del Prof. Felipe González; ITAM MCD2023

Here you will find my Data science and Machine learning Projects

가짜연구소 <인과추론과 실무> 프로젝트

Conducted an experiment to assess the impact of music on cognitive function through the performance of activities that require logical reasoning

Researching causal relationships in time series data using Temporal Convolutional Networks (TCNs) combined with attention mechanisms. This approach aims to identify complex temporal interactions. Additionally, we're incorporating uncertainty quantification to enhance the reliability of our causal predictions.