The Causal Inference Tool is an interactive and data-driven solution designed to help analysts, researchers, and decision-makers explore causal relationships between variables in a dataset. This tool allows users to input data for treatment, outcome, and confounding variables to estimate causal effects using methods such as propensity score matching, difference-in-differences (DiD), or regression-based approaches. It provides actionable insights into the impact of interventions or treatments and helps stakeholders make informed decisions.

Key Features:

1. Dynamic Input Fields: Users can input datasets with treatment, outcome, and confounding variables.
2. Causal Effect Estimation: Automatically estimate causal effects using methods like propensity score matching, DiD, or regression adjustment.
3. Interactive Charts: Visualize causal relationships and treatment effects using scatter plots, bar charts, or time-series graphs for enhanced clarity.
4. Scenario Testing: Allow users to adjust inputs dynamically to explore how changes in treatment or confounders affect causal estimates.
5. PDF Export Functionality: Generate downloadable reports summarizing causal inference results for presentations or sharing with stakeholders.
6. Modern and Stylish Design: A sleek interface with vibrant colors, animations, and clear typography to enhance user engagement.
7. Fully Responsive: Optimized for all devices, ensuring seamless functionality on desktops, tablets, and mobiles.
8. Self-Contained Container: The tool operates within its own container, ensuring no interference with the page header or footer.

Use Cases:

• Researchers analyzing the impact of policy interventions on outcomes.
• Businesses evaluating the effectiveness of marketing campaigns.
• Healthcare analysts assessing the impact of treatments on patient outcomes.

How It Works:
Users input data for treatment, outcome, and confounding variables. The tool applies causal inference methods such as propensity score matching, DiD, or regression adjustment to estimate the causal effect of the treatment on the outcome. Results are displayed in tables and visualizations, allowing users to interpret the causal relationship. Users can customize inputs dynamically, analyze scenarios, and download detailed reports in PDF format for further analysis or sharing with stakeholders.