Statistics / Causal Inference
Causal Inference
When a randomized experiment isn't possible — a policy already rolled out, users who self-selected into a feature, an external event that hit one group but not another — you're left with observational data, and correlation alone can't tell you whether an effect is real. Causal inference gives you the toolkit to extract a genuine causal estimate from that observational data, provided its assumptions actually hold for your situation. We help you choose the right method, verify its assumptions, and interpret the result correctly.
This Is For You If...
You need to know whether a feature, policy, or intervention actually caused a change, using data that wasn't from a randomized experiment
You have panel data before and after a policy change and want to isolate its true effect
You suspect your treatment and outcome share a confounding variable and need a rigorous way to address it
You have a working causal analysis but aren't confident its key assumptions actually hold
Which Method Fits Your Situation?
Ask Yourself | Points Toward |
Do you want to control for observed confounders directly within a regression framework? | Causal Regression |
Do you have observed characteristics that predict who received treatment, and want to balance groups on them? | Propensity-Score Methods |
Do you want to directly pair treated and untreated units with similar observed characteristics? | Matching |
Do you have data on the same units before and after treatment, plus a comparable untreated group? | Difference-in-Differences |
Do you suspect a variable that affects treatment but not the outcome directly, that you could use to isolate the causal effect? | Instrumental Variables |
Do you have just one treated unit (a single region, company, or country) and need a data-driven counterfactual? | Synthetic Controls |
The right method depends entirely on what variation in your data is plausibly free of confounding — there's no universally best method, only the one that fits your actual data structure and what you can credibly assume. Tell us your situation and we'll help identify the right approach, or combination.
Causal Inference Services We Offer
Causal Regression
Causal regression approaches estimate a treatment effect by controlling for observed confounding variables directly within a regression model, relying on the assumption that you've measured and included everything that jointly affects both treatment assignment and the outcome — the selection-on-observables assumption. We help you specify the regression correctly, identify what needs to be controlled for, and be honest about the assumption's limits — since anything unmeasured that affects both treatment and outcome will still bias the result.
Propensity-Score Methods
Propensity-score methods estimate the probability of receiving treatment given observed characteristics — the propensity score e(X) — and use it to balance treatment and control groups on those characteristics, through approaches like Inverse Probability Weighting (IPW) or doubly robust estimation, which combines an outcome model with IPW for extra protection against model misspecification. We help you estimate propensity scores properly, check that they actually balance your groups, and choose the right estimator for your target parameter (typically the Average Treatment Effect or Average Treatment Effect on the Treated).
Matching
Matching methods directly pair treated and untreated units with similar observed characteristics — using techniques like nearest-neighbor matching, Mahalanobis distance matching, or propensity score matching specifically, along with caliper restrictions and trimming to improve match quality. We help you choose the right matching approach for your data, assess the quality of the resulting matched sample, and interpret the treatment effect estimate correctly, typically the effect on the treated (ATT).
Difference-in-Differences
Difference-in-differences (DiD) compares the change in outcome for a treated group before and after treatment against the change for a comparable untreated group over the same period, with the control group's change removing common time trends to isolate the treatment effect — a widely used method in policy evaluation. It critically depends on the parallel trends assumption — that treated and control groups would have followed similar trends absent treatment — which we help you check with formal pre-trend tests, not just a visual inspection. We also help address complications like staggered treatment timing, where standard DiD can produce misleading results without appropriate modern methods.
Instrumental Variables
Instrumental variables (IV) estimation uses a variable that affects treatment assignment but influences the outcome only through that treatment (the exclusion restriction) to isolate a causal effect even when unobserved confounding exists — commonly implemented through two-stage least squares (2SLS). We help you identify and justify a credible instrument for your specific situation, test for weak instruments (which can badly distort inference), and correctly interpret the resulting estimate — which is typically the Local Average Treatment Effect (LATE) for the subset of "compliers," not the average effect for everyone.
Synthetic Controls
The synthetic control method constructs a data-driven counterfactual for a single treated unit (a region, company, or country) by building a weighted combination of untreated "donor" units that closely reproduces the treated unit's pre-treatment trends, then compares actual post-treatment outcomes to that synthetic counterfactual. We help you select appropriate donor units, construct the synthetic control properly, and run the inference procedures needed to assess whether the resulting effect estimate is statistically meaningful.
Not sure which method fits, or need help choosing between several plausible approaches for the same question? That's one of the most important and common questions in causal inference — describe your data structure and what variation you have, and we'll help identify the most credible approach.
What We Need From You
What to Share | Why It Helps |
A description of the treatment/intervention and the outcome you're studying | Central to identifying which causal inference method actually fits your situation |
Your data structure (cross-sectional, panel/before-after, single treated unit) | Different methods require different data structures — this often narrows down the right approach quickly |
Any observed variables that might predict both treatment and outcome | Needed for propensity-score methods, matching, and assessing selection-on-observables assumptions |
Whether you have a plausible instrument in mind, if considering IV | Central to assessing whether IV is viable, and whether the instrument is actually credible |
Any existing analysis and what result you're trying to validate or improve | Lets us diagnose specific assumption violations rather than starting from scratch |
Your deadline | Helps us scope the right turnaround |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Treating any observed correlation as evidence of a causal effect | Correlation alone can't distinguish a real causal effect from confounding or reverse causation | Use an appropriate causal identification strategy, not just a regression coefficient, to support a causal claim |
Assuming parallel trends hold for a difference-in-differences design without checking | If treated units were already on a different trajectory before treatment, DiD will misattribute that pre-existing difference to the treatment | Formally test pre-treatment trends, not just visually inspect them, before relying on a DiD estimate |
Using a weak instrument for IV estimation | Weak instruments (that barely predict treatment) produce unreliable, often badly biased estimates, even when the theory seems sound | Test instrument strength formally, and be skeptical of instruments that only weakly predict treatment |
Interpreting an IV or DiD estimate as the average treatment effect for everyone | IV typically estimates the LATE for "compliers," and DiD typically estimates the ATT — both narrower parameters than a population-wide average effect | Be precise about which causal parameter your method actually estimates, and interpret results accordingly |
Choosing matching or propensity-score methods without checking whether groups are actually well-balanced afterward | An unbalanced matched sample can still carry significant confounding bias, defeating the purpose of matching | Formally assess covariate balance after matching or weighting, not just assume the procedure worked |
Picking a causal method based on familiarity rather than what your data structure and credible assumptions actually support | The wrong method for your data structure can produce a confidently wrong answer | Choose the method whose required assumptions are genuinely plausible for your specific data and context |
How It Works
1. Share your question and data
Tell us the treatment, outcome, and data structure you're working with
2. We identify the right identification strategy
We confirm which causal method's assumptions are actually credible for your situation, given your data
3. We implement and test assumptions rigorously
We run the analysis and formally check the method's key assumptions (parallel trends, instrument strength, covariate balance), not just assume they hold
4. We interpret the result precisely
We're explicit about exactly which causal parameter was estimated (ATE, ATT, LATE) and what that does and doesn't tell you
5. We explain the reasoning clearly
You get a plain-English explanation of the method, its assumptions, and what can be credibly concluded, suitable for a paper, a report, or a real decision
Why Codersarts
Causal inference is easy to get subtly wrong — a DiD estimate with untested parallel trends, an IV analysis with a weak instrument, a propensity-score approach with unbalanced groups afterward. We choose methods based on what your actual data structure and credible assumptions support, test those assumptions formally rather than assuming they hold, and are precise about exactly which causal parameter any given method estimates. We've supported researchers and analysts making real policy, product, and business decisions where getting the causal claim right genuinely mattered.
Turnaround Time
Well-defined analyses with available data and a clear identification strategy are typically turned around within a week. More involved work — identifying and validating an instrument, constructing a synthetic control, addressing staggered treatment timing — is scoped with a clear timeline once we understand the problem. Paper and launch deadlines are prioritized accordingly — let us know your timeline upfront.
FAQ
I don't know which causal method fits my data — can you help figure that out?
Yes — this is one of the most common and important starting points. Describe your treatment, outcome, and data structure, and we'll help identify the most credible approach.
Do you use specific tools for these methods?
Yes — we work with Python (linearmodels, econml, DoWhy), R (MatchIt, WeightIt, Synth/augsynth), and Stata, depending on your context and preference.
How rigorous is this — will it hold up in a paper or a real business decision?
We test each method's key assumptions formally (parallel trends tests, instrument strength tests, covariate balance checks) rather than assuming they hold, so the analysis is defensible either academically or practically.
Is this for coursework, or only real research/business applications?
Both — we support academic coursework and research in causal inference and econometrics, as well as real policy, product, and business causal analyses.
What's the difference between the effect your method estimates and a simple "average effect"?
Different methods estimate different causal parameters (ATE, ATT, LATE) — we're explicit about which one applies to your result, since conflating them is a common source of misinterpretation.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Need to Know if Something Actually Caused an Outcome?
Whether it's research or a real policy or business decision — get a causal estimate you can actually defend.