# Behavior Learning

This repository contains the supplementary code for the ICLR 2026 submission **"Behavior Learning"**.

## Repository Structure

```
Behavior_Learning/
├── counterfactual_prediction_experiments/     
├── high_dimensional_experiments/    
├── standard_prediction_experiments/    
├── requirements.txt                
└── README.md                        
```

## How to Reproduce Results

### Environment Setup

**Step 1.** Clone or download this repository.

**Step 2.** Set up Python environment (Python 3.8+ recommended).

**Step 3.** Install dependencies.

### Data Setup

## Experiments Overview

### 1. Standard Prediction Experiments

**Location:** `standard_prediction_experiments/`

**Purpose:** Comparison of BL methods against standard baselines on tabular datasets.

**Datasets:** Adult Income, German Credit, Wine Quality, Bank Marketing, etc.

### 2. High-Dimensional Experiments

**Location:** `high_dimensional_experiments/`

**Purpose:** Compare BL methods with energy-based MLP baselines on high-dimensional data (images and text).

### 3. Counterfactual Prediction Experiments

**Location:** `counterfactual_prediction_experiments/`

**Purpose:** Compare CausalBL with counterfactual prediction methods for treatment effect estimation.


## Important Note

Due to security considerations, the core Behavior Learning (BL) implementation is not included in this supplementary code submission. **We guarantee that the complete BL implementation will be made publicly available on GitHub immediately upon paper acceptance.**
