# Causal Reviews **Vendor:** Causal Labs **Category:** [Feature Management Software](https://www.g2.com/categories/feature-management) **Average Rating:** 5.0/5.0 **Total Reviews:** 4 ## About Causal Causal is an integrated set of product analytics and feature management tools for deploying, measuring, and optimizing product features. It is designed for companies who need to ship and iterate quickly. Unlike other feature management products, Causal covers the entire product lifecycle and automates much of the custom, manual work involved in managing product features. Using Causal helps teams write more maintainable code, collect better data with less effort, and run effective experiments quickly. ## Causal Reviews ### 1. Engaged Company Strongly Supporting New Product **Rating:** 5.0/5.0 stars **Reviewed by:** Jim G. | Mid-Market (51-1000 emp.) **Reviewed Date:** August 25, 2023 **What do you like best about Causal?** For me, the best part about Causal Labs product has been their employees. Everyone I've interacted with there has been highly engaged, curious about how their work is perceived, how people use it, and whatever they can do to make it better. The employees are all strongly committed to their clients, trying to make their lives better by adding value in solving the problems that need to be solved. **What do you dislike about Causal?** Because it's still early in the life of Causal Labs, the focus is on creating core solutions that meet as many needs as possible. This currently gives it a focus on being an "expert tool for experts", which is fine unless you're trying to onboard people into the problem space. The Causal Labs team is highly engaged in trying to train people as needed, and they're passionate about their solution such that I suspect they will make it easier and more robust with time, **What problems is Causal solving and how is that benefiting you?** Causal Labs' solution is enabling our social media site experimentation, covering everything from simple feature flags to ensuring that experiments are run against A/B tests, and that we get lots of useful data back delivered easily so we can make decisions quickly. ### 2. Excellent, focused product that makes accurate A/B testing faster and easier across the tech stack. **Rating:** 5.0/5.0 stars **Reviewed by:** Tony D. | Mid-Market (51-1000 emp.) **Reviewed Date:** August 17, 2023 **What do you like best about Causal?** 1. Built-in statistical modeling that avoids common pitfalls in analyzing A/B tests. 2. Encourages structured, well-defined A/B tests. 3. Makes it easy to run tests that cross layers of the stack. 4. Provides easy integration with our data warehouse, even for event generation that is unrelated to a specific A/B test. 5. Installation of impression servers in our own cloud means that it has a very low impact on performance. 6. Causal team is fantastic and very responsive, including supporting our internal training and adoption. **What do you dislike about Causal?** 1. It requires a significant investment in training engineers and product managers to incorporate it into your workflows. 2. The UX isn't as smooth and polished yet as some it could be. **What problems is Causal solving and how is that benefiting you?** We're an organization that continually experiments and runs quantitative A/B tests. Before Causal, we had 4 A/B testing platforms, some home-grown, some commercial, that were used in different product areas. Generally, these platforms required manual analysis of the results, and allowed common failure modes (e.g., using standard stats and peeking at the results early). In addition, we have a totaly schema-less, documentation-free "reportable event" API that was used in conjunction with these tools to provide ancillary information, which also has to be analyzed manually (and is almost never cleaned up after the fact). Causal provides us with a single platform across the stack, statistical tools, and a documented mechanism for events, which allow us to move faster and with greater confidence in the accuracy of our tests. ### 3. Causal: Empowering decision-making with streamlines workflow **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** August 28, 2023 **What do you like best about Causal?** We have been utilizing Causal for conducting feature and A/B tests. This tool has aided out decision-making process by providing detailed insights into each feature's performance. These insights empower us to make well informed, data driven decisions. Im a huge fan of Causals user interface, that has allowed us to directly set the new control value. This feaure not only expidites the decision-making process but also eliminates the need for code modifications after a decisions is made. This efficiency saves us valuable time and resources, ultimately streamlining our workflow. **What do you dislike about Causal?** I encountered a bit of a learning curve with Causal, but appreciated that the platform provided resources to get me up to speed. Causals documentation is well structured and comprehensive, making the process of getting acquainted with these technologies manageable. The resources provided allow users to bridge the knowledge gap effectively. **What problems is Causal solving and how is that benefiting you?** Causal serves as a transformative feature testing and development solution, empowering us to make data driven decisions with confidence. It foes beyond traditional feature flags by offering structure features that surpass their capabilities. The platforms remarkable support for React, Typescript and server rendering ensures seamless integration into our tech stack, while its exceptional support network further enhaces the overall experience. ### 4. Great testing platform with impactful analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Brett M. | Mid-Market (51-1000 emp.) **Reviewed Date:** July 24, 2023 **What do you like best about Causal?** Once up and running on Causal it was easy for anyone on the product and engineering teams to effectively UAT upcoming tests, iterate on new variants within a test concept, and review results effectively. The Causal team was very responsive to our needs and we were actively in dialog on ways to improve the platform. **What do you dislike about Causal?** It's a newer platform, so some features were still being developed as we onboarded with Causal. However, the team was always responsive to our requests and there was never anything missing from the platform that was an impediment to our immediate needs. **What problems is Causal solving and how is that benefiting you?** Causal was our A/B testing platform. We used to setup, run and review A/B tests at a consumer-facing tech company. This allowed for the effective distribution of A/B test setup and management across product and engineering. - [View Causal pricing details and edition comparison](https://www.g2.com/products/causal-labs-causal/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-21+23%3A30%3A48+-0500&secure%5Bsession_id%5D=086c7375-511d-4423-8658-6f39282dbd2b&secure%5Btoken%5D=43ba6b70cc951658e7944736add33727e81589e26d5bb9c39db036d42fe54f45&format=llm_user) ## Causal Features **Management** - Flag Management - Rollout & Rollback Control - Monitoring **Functionality** - Multi-Environment Control - Feature Testing - Low-Code Interface ## Top Causal Alternatives - [PostHog](https://www.g2.com/products/posthog/reviews) - 4.5/5.0 (1,040 reviews) - [VWO Testing](https://www.g2.com/products/wingify-vwo-testing/reviews) - 4.4/5.0 (914 reviews) - [LaunchDarkly](https://www.g2.com/products/launchdarkly/reviews) - 4.5/5.0 (717 reviews)