causality

Causal Inference One-to-one Matching on Confounders Using Python and R. Using R Matching package for causal inference with Mahalanobis Distance Matching (MDM) in Google Colab notebook.

Causal Inference One-to-one Matching on Confounders Using Python and R

Causal inference is the process of determining the effect of a treatment. The causal impact can be evaluated by randomized experiments or observational studies. In this tutorial, we will talk about how to use Mahalanobis Distance Matching (MDM) for causal inference using the R package Matching. You will learn: Resources for this post: Let’s get started! …

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Causal Inference One-to-one Propensity Score Matching Using R MatchIt Package. How both Python users and R users can use R MatchIt package for causal inference with Propensity Score Matching (PSM)

Causal Inference One-to-one Propensity Score Matching Using R MatchIt Package

Propensity Score Matching (PSM) for causal inference using the R MatchIt package is introduced in this tutorial. Causal inference has well-established packages in R, but not in Python. This tutorial provides an example of using R packages for causal analysis in a Python notebook. In this tutorial you will learn: Resources for this post: Step …

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Causal Inference Logit Propensity Score Matching (PSM). How can Python and R users use the R Matching package for causal inference with logit Propensity Score Matching (PSM)?

Causal Inference Logit Propensity Score Matching (PSM)

How can Python and R users use the R Matching package for causal inference with logit Propensity Score Matching (PSM)? Causal inference has well-established packages in R, but not in Python. This tutorial provides an example of using R packages for causal analysis in a Python notebook. In this tutorial, you will learn: Resources for …

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Propensity Score Trimming Using Python Package Causal Inference. Use the Python CausalInference package to estimate propensity scores, trim extreme values, improve balances between treatment and control, and evaluate treatment effects

Propensity Score Trimming Using Python Package Causal Inference

CausalInference is a Python package for causal analysis. It has different functionalities such as propensity score trimming, covariates matching, counterfactual modeling, subclassification, and inverse probability weighting. In this tutorial, we will talk about how to do propensity score trimming using CausalInference, and how that impacts the causal impact analysis results. Other functionalities will be introduced in future …

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ATE vs CATE vs ATT vs ATC for Causal Inference. Average Treatment Effect (ATE), Conditional Average Treatment Effect (CATE), Average Treatment Effect on the Treated (ATT), and Average Treatment Effect on the Control (ATC) for Causal Analysis

ATE vs CATE vs ATT vs ATC for Causal Inference

Average Treatment Effect (ATE), Conditional Average Treatment Effect (CATE), Average Treatment Effect on the Treated (ATT), and Average Treatment Effect on the Control (ATC) are commonly used concepts for causal impact analysis. It’s essential to understand these concepts to correctly interpret the causal inference results. In this tutorial, we will talk about the definitions and …

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OLS Treatment Effects Estimation Using Python Package Causal Inference. Estimate treatment effects using ordinary least squares (OLS) in Python

OLS Treatment Effects Estimation Using Python Package Causal Inference

CausalInference is a Python package for causal analysis. It has different functionalities such as propensity score trimming, covariates matching, ordinary least squares (OLS) treatment effects estimation, subclassification, and inverse probability weighting. In this tutorial, we will talk about how to do ordinary least squares (OLS) treatment effects estimation. Other functionalities will be introduced in future tutorials. …

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Subclassification Propensity Score Matching Using Python Package Causal Inference. Propensity score estimation, subclassification matching, and treatment effect estimation

Subclassification Propensity Score Matching Using Python Package Causal Inference

Subclassification matching in causal inference stratifies the propensity scores into bins, and the treatment and the control units within the bins are compared to get the treatment effects estimation. In this tutorial, we will talk about how to do subclassification propensity score matching (PSM) using the Python CausalInference package. To learn how to do subclassification matching using …

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Inverse Probability Treatment Weighting (IPTW) Using Python Package Causal Inference. Causality analysis of treatment effects using Inverse Probability Treatment Weighting (IPTW) in Python

Inverse Probability Treatment Weighting (IPTW) Using Python Package Causal Inference

Inverse Probability Treatment Weighting (IPTW) is a statistical method for causal analysis. In this tutorial, we will talk about how to do Inverse Probability Treatment Weighting (IPTW) using the Python CausalInference package. Resources for this post: Let’s get started! Step 1: Install and Import Libraries In step 1, we will install and import libraries. Firstly, …

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One-to-one Matching on Confounders Using Python Package Causal Inference. Bias-adjusted one-to-one and one-to-many matching on Confounders in python

One-to-one Matching on Confounders Using Python Package Causal Inference

One-to-one matching on confounders takes a sample in the treatment group, and finds a similar sample in the non-treatment group based on the confounder similarities. The goal of matching is to create a synthetic control group that is comparable to the treatment group. In this tutorial, we will talk about how to do one-to-one matching …

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