diff --git a/src/counterfactual_mean_based/estimators.jl b/src/counterfactual_mean_based/estimators.jl
index fb768c00..e06fb92e 100644
--- a/src/counterfactual_mean_based/estimators.jl
+++ b/src/counterfactual_mean_based/estimators.jl
@@ -3,6 +3,7 @@
 #####################################################################
 struct FitFailedError <: Exception
     estimand::Estimand
+    model::MLJBase.Model
     msg::String
     origin::Exception
 end
@@ -13,6 +14,11 @@ outcome_mean_fit_error_msg(factor) = string(
     string_repr(factor), 
     ".\n Hint: don't forget to use `with_encoder` to encode categorical variables.")
 
+outcome_mean_fluctuation_fit_error_msg(factor) = string(
+    "Could not fluctuate the following Outcome mean: ", 
+    string_repr(factor), 
+    ".")
+
 Base.showerror(io::IO, e::FitFailedError) = print(io, e.msg)
 
 struct CMRelevantFactorsEstimator <: Estimator
@@ -67,7 +73,8 @@ function (estimator::CMRelevantFactorsEstimator)(estimand, dataset; cache=Dict()
     # Fit propensity score
     propensity_score_estimate = map(estimand.propensity_score) do factor
         try
-            ConditionalDistributionEstimator(train_validation_indices, acquire_model(models, factor.outcome, dataset, true))(
+            ps_estimator = ConditionalDistributionEstimator(train_validation_indices, acquire_model(models, factor.outcome, dataset, true))
+            ps_estimator(
                 factor,    
                 dataset;
                 cache=cache,
@@ -75,19 +82,21 @@ function (estimator::CMRelevantFactorsEstimator)(estimand, dataset; cache=Dict()
                 machine_cache=machine_cache
             )
         catch e
-            throw(FitFailedError(factor, propensity_score_fit_error_msg(factor), e))
+            model = acquire_model(models, factor.outcome, dataset, true)
+            throw(FitFailedError(factor, model, propensity_score_fit_error_msg(factor), e))
         end
     end
     # Fit outcome mean
     outcome_mean = estimand.outcome_mean
     model = acquire_model(models, outcome_mean.outcome, dataset, false)
+    outcome_mean_estimator = ConditionalDistributionEstimator( 
+        train_validation_indices, 
+        model
+    )
     outcome_mean_estimate = try
-        ConditionalDistributionEstimator( 
-            train_validation_indices, 
-            model
-        )(outcome_mean, dataset; cache=cache, verbosity=verbosity, machine_cache=machine_cache)
+        outcome_mean_estimator(outcome_mean, dataset; cache=cache, verbosity=verbosity, machine_cache=machine_cache)
     catch e
-        throw(FitFailedError(outcome_mean, outcome_mean_fit_error_msg(outcome_mean), e))
+        throw(FitFailedError(outcome_mean, model, outcome_mean_fit_error_msg(outcome_mean), e))
     end
     # Build estimate
     estimate = MLCMRelevantFactors(estimand, outcome_mean_estimate, propensity_score_estimate)
@@ -115,13 +124,19 @@ TargetedCMRelevantFactorsEstimator(Ψ, initial_factors_estimate; tol=nothing, ps
 
 function (estimator::TargetedCMRelevantFactorsEstimator)(estimand, dataset; cache=Dict(), verbosity=1, machine_cache=false)
     model = estimator.model
+    outcome_mean = model.initial_factors.outcome_mean.estimand
     # Fluctuate outcome model
-    fluctuated_outcome_mean = MLConditionalDistributionEstimator(model)(
-        model.initial_factors.outcome_mean.estimand,
-        dataset,
-        verbosity=verbosity,
-        machine_cache=machine_cache
-    )
+    fluctuated_estimator = MLConditionalDistributionEstimator(model)
+    fluctuated_outcome_mean = try
+        fluctuated_estimator(
+            outcome_mean,
+            dataset,
+            verbosity=verbosity,
+            machine_cache=machine_cache
+        )
+    catch e
+        throw(FitFailedError(outcome_mean, model, outcome_mean_fluctuation_fit_error_msg(outcome_mean), e))
+    end
     # Do not fluctuate propensity score
     fluctuated_propensity_score = model.initial_factors.propensity_score
     # Build estimate
diff --git a/test/counterfactual_mean_based/estimators_and_estimates.jl b/test/counterfactual_mean_based/estimators_and_estimates.jl
index c8600e09..4e4e6858 100644
--- a/test/counterfactual_mean_based/estimators_and_estimates.jl
+++ b/test/counterfactual_mean_based/estimators_and_estimates.jl
@@ -10,6 +10,9 @@ using MLJLinearModels
 using CategoricalArrays
 using LogExpFunctions
 using MLJBase
+using CSV
+
+DATADIR = joinpath(pkgdir(TMLE), "test", "data")
 
 function make_dataset()
     n = 100
@@ -55,7 +58,6 @@ end
     @test TMLE.key(η, new_η̂) == TMLE.key(η, η̂)
     full_reuse_log = (:info, TMLE.reuse_string(η))
     @test_logs full_reuse_log new_η̂(η, dataset; cache=cache, verbosity=1)
-
     # Changing one model, only the other one is refitted
     new_models = (
         Y = with_encoder(LinearRegressor()), 
@@ -85,7 +87,7 @@ end
     Q = TMLE.ConditionalDistribution(:Y, [:T₁, :W])
     G = (TMLE.ConditionalDistribution(:T₁, [:W]),)
     η = TMLE.CMRelevantFactors(outcome_mean=Q, propensity_score=G)
-    # Estimator
+    # Propensity score model is ill-defined
     models = (
         Y = with_encoder(LinearRegressor()), 
         T₁ = LinearRegressor()
@@ -96,15 +98,44 @@ end
         @test true === false
     catch e
         @test e isa TMLE.FitFailedError
+        @test e.model isa LinearRegressor
         @test e.msg == TMLE.propensity_score_fit_error_msg(G[1])
     end
-
+    # Outcome Mean model is ill-defined
     models = (
         Y = LogisticClassifier(), 
         T₁ = LogisticClassifier(fit_intercept=false)
     )
     η̂ = TMLE.CMRelevantFactorsEstimator(models=models)
-    @test_throws TMLE.FitFailedError η̂(η, dataset; verbosity=0)
+    try 
+        η̂(η, dataset; verbosity=0)
+        @test true === false
+    catch e
+        @test e isa TMLE.FitFailedError
+        @test e.model isa LogisticClassifier
+        @test e.msg == TMLE.outcome_mean_fit_error_msg(Q)
+    end
+    # Fluctuation Pos Def Exception
+    pos_def_error_dataset = CSV.read(joinpath(DATADIR, "posdef_error_dataset.csv"), DataFrame)
+    outcome = Symbol("G25 Other extrapyramidal and movement disorders")
+    treatment = Symbol("2:14983:G:A")
+    pos_def_error_dataset[!, treatment] = categorical(pos_def_error_dataset[!, treatment])
+    pos_def_error_dataset[!, outcome] = categorical(pos_def_error_dataset[!, outcome])
+    Ψ = ATE(
+        outcome=outcome, 
+        treatment_values = NamedTuple{(treatment,)}([(case = "GG", control = "AG")]), 
+        treatment_confounders = (:PC1, :PC2, :PC3, :PC4, :PC5, :PC6)
+    )
+    Q = TMLE.ConditionalDistribution(outcome, [treatment, :PC1, :PC2, :PC3, :PC4, :PC5, :PC6])
+    tmle = TMLEE(models=TMLE.default_models(Q_binary=LogisticClassifier(), G = LogisticClassifier()))
+    try 
+        tmle(Ψ, pos_def_error_dataset)
+        @test true === false
+    catch e
+        @test e isa TMLE.FitFailedError
+        @test e.model isa TMLE.Fluctuation
+        @test e.msg == TMLE.outcome_mean_fluctuation_fit_error_msg(Q)
+    end
 end
 
 @testset "Test structs are concrete types" begin
diff --git a/test/data/posdef_error_dataset.csv b/test/data/posdef_error_dataset.csv
new file mode 100644
index 00000000..8d3771d4
--- /dev/null
+++ b/test/data/posdef_error_dataset.csv
@@ -0,0 +1,501 @@
+PC4,PC1,PC5,2:14983:G:A,G25 Other extrapyramidal and movement disorders,PC3,PC6,PC2
+0.185222,-0.4151422,-0.2173696,GG,false,-0.05562614,0.1557684,0.2614491
+-0.07582313,0.04855956,0.03686971,GG,false,-0.07382229,0.1173217,-0.1278596
+-0.03794112,0.1273501,-0.2127753,GG,false,0.1318188,0.05557316,-0.09651864
+0.00617583,-0.1465271,-0.2026204,GG,false,0.1940618,-0.0336929,-0.1270814
+-0.1376979,-0.2959911,0.1489762,GG,false,-0.04980811,0.2536836,0.00627186
+0.016602,-0.3907019,0.04016818,GG,false,-0.003498581,0.01162162,-0.2542934
+0.002090582,-0.2531442,0.05252894,GG,false,0.05609067,0.07235009,0.02764647
+-0.07959207,-0.1233443,0.2283963,GG,false,-0.1045894,-0.008902588,-0.0160001
+-0.06804591,-0.08434612,0.1602938,GG,false,-0.1161518,0.1856726,0.03011304
+-0.1141844,-0.2069707,0.004859168,GG,false,-0.04674481,0.04697416,-0.006524136
+0.02311513,-0.09775404,0.04833003,GG,false,-0.05905194,0.2197438,-0.1985185
+0.09675734,0.03151001,0.001348238,GG,false,-0.1913851,0.02653726,-0.2404906
+-0.1373833,-0.1051594,-0.06473765,GG,false,-0.07683287,-0.1790446,0.1459381
+0.1055764,0.1826204,-0.07598882,GG,false,0.1328787,0.02236083,-0.1031464
+-0.053281,-0.02158421,0.04252805,GG,false,0.07808154,0.2305702,0.07926224
+0.07466328,-0.2576077,0.1002832,GG,false,0.125019,0.02034366,-0.01598918
+-0.04880092,-0.2154424,-0.03378025,GG,false,-0.05006283,-0.01081544,0.0357233
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