Raspberry Pi 5 Causal Inference IoT Research Kit: Go Beyond Correlation with DoWhy and ESP32 Sensors

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

In agriculture, industrial IoT, and smart cities, it’s not enough to see that temperature and humidity move together — you need to know if one sensor’s change actually causes the other. This kit gives you a complete hardware-software stack to move beyond correlation. You’ll deploy three ESP32 sensor nodes streaming DHT22 (temp/humidity) and ADXL345 (acceleration) data to a Raspberry Pi 5 with NVMe storage, then run the DoWhy causal inference library to model, identify, and estimate causal effects. Whether you’re validating a hypothesis for a hackathon or preparing for a research publication, you’ll have a reproducible, local pipeline that turns raw IoT data into defensible causal conclusions.

What You'll Build

A multi-node IoT causal discovery system. Three ESP32 boards collect temperature, humidity, and vibration from separate physical locations and send data wirelessly to the Pi 5. You’ll set up the DoWhy environment, define causal graphs (DAGs) based on your domain theory, and compute average treatment effects and refutation tests. The result is an interactive Jupyter dashboard that visualizes full causal pathways — not just correlation heatmaps — so you can definitively say “increasing ventilation reduces humidity by X%” and prove it statistically.

What You'll Learn

• Configure a Raspberry Pi 5 with NVMe SSD for high-speed sensor data ingestion and causal model execution
• Program ESP32 microcontrollers to read DHT22 and ADXL345 sensors and transmit data over Wi-Fi using MQTT
• Apply DoWhy’s four-step causal modeling (model, identify, estimate, refute) to real-world IoT time series
• Interpret directed acyclic graphs and differentiate confounders, mediators, and colliders from multi-sensor data

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

B.Tech, M.Tech, and PhD students in ECE, CS, and data science who need to demonstrate causal inference on real hardware. It’s equally suited for Smart India Hackathon teams building IoT‑driven solutions for smart farming or Industry 4.0, and for research groups at IITs, NITs, VIT, and BITS Pilani looking to publish reproducible causal experiments. If you’ve outgrown simple sensor projects and want to add rigorous statistical methodology to your portfolio, this kit is your launchpad.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.